Review Article Influential Usage of Big Data and Artificial Intelligence in Healthcare

Page created by Fred Griffin
 
CONTINUE READING
Review Article Influential Usage of Big Data and Artificial Intelligence in Healthcare
Hindawi
Computational and Mathematical Methods in Medicine
Volume 2021, Article ID 5812499, 13 pages
https://doi.org/10.1155/2021/5812499

Review Article
Influential Usage of Big Data and Artificial
Intelligence in Healthcare

          Yan Cheng Yang ,1,2 Saad Ul Islam,3 Asra Noor,3 Sadia Khan,3 Waseem Afsar,3
          and Shah Nazir 3
          1
            Foreign Language Department, Luoyang Institute of Science and Technology, Luoyang, Henan, China
          2
            Foreign Language Department/Language and Cognition Center, Hunan University, Changsha, Hunan, China
          3
            Department of Computer Science, University of Swabi, Swabi, Pakistan

          Correspondence should be addressed to Yan Cheng Yang; 185342350@qq.com and Shah Nazir; shahnazir@uoswabi.edu.pk

          Received 8 July 2021; Accepted 9 August 2021; Published 6 September 2021

          Academic Editor: Muhammad Zubair Asghar

          Copyright © 2021 Yan Cheng Yang et al. This is an open access article distributed under the Creative Commons Attribution
          License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is
          properly cited.

          Artificial intelligence (AI) is making computer systems capable of executing human brain tasks in many fields in all aspects of
          daily life. The enhancement in information and communications technology (ICT) has indisputably improved the quality of
          people’s lives around the globe. Especially, ICT has led to a very needy and tremendous improvement in the health sector
          which is commonly known as electronic health (eHealth) and medical health (mHealth). Deep machine learning and AI
          approaches are commonly presented in many applications using big data, which consists of all relevant data about the medical
          health and diseases which a model can access at the time of execution or diagnosis of diseases. For example, cardiovascular
          imaging has now accurate imaging combined with big data from the eHealth record and pathology to better characterize the
          disease and personalized therapy. In clinical work and imaging, cancer care is getting improved by knowing the tumor biology
          and helping in the implementation of precision medicine. The Markov model is used to extract new approaches for leveraging
          cancer. In this paper, we have reviewed existing research relevant to eHealth and mHealth where various models are discussed
          which uses big data for the diagnosis and healthcare system. This paper summarizes the recent promising applications of AI
          and big data in medical health and electronic health, which have potentially added value to diagnosis and patient care.

1. Introduction                                                       sources and quality of life data [1] as well as the unprece-
                                                                      dented and quantitative datasets of neuroscience [2]. Medical
AI is a field of computer science, which is capable of copying         care and treatment are getting enhanced day by day.
human characteristics, capacity of learning, and storage of           Researchers are collecting data to make human care and
knowledge. It executes human brain tasks in most of the               diagnosis disease efficiently and operationally. Medical doc-
fields in all aspects of our daily life using big data applica-        tors are skeptical about the help that artificial intelligence
tions. Super computers are analyzing big data using the algo-         can deliver to them in their medical practices. Studies have
rithms of advanced deep learning machines, which has                  explored an understandable experience which includes all
allowed the improvement of output in the field. This field              challenges, risks, and comfort since the implementation of
has led very needy and tremendous enhancement in every                AI programmed with the help of big data in healthcare within
field of life, especially in healthcare. Big data analysis is          a contagious disease setting. The aim of the program was at
entered in biomedical sciences; the foremost source of big            designing a set of tools which can support an accurate, objec-
data has been discussed and explained, especially in oncol-           tive, and clinical decision-making process efficiently [3]. In
ogy, cardiovascular disease, allergic diseases, clinical work,        biomedicine, there are many ways to collect data from
ear diseases, and so on. Along with this, it also touches some        observation and experiments. Scientists are often struggling
aspects of need to combine several pathological and clinical          to collect data about the disease and on how data are being
Review Article Influential Usage of Big Data and Artificial Intelligence in Healthcare
2                                                                      Computational and Mathematical Methods in Medicine

generated for the treatment of disease. This biomedical infor-     edge extraction by examining clinical datasets to define the
mation is to create thinking, managing, and analyzing and          decisive features using modeling techniques, capable of han-
the way they can transform into further scientific perception       dling and analyzing large-scale data in its context [10]. In the
for enhancing patient care. This is the major challenge for the    analysis, guidelines for using AI to speed up the drug’s
National Institutes of Healthcare (NIH) to lead the big data       recovery, so that AI’s vision is not strong but important,
to knowledge representation. The agencies are making efforts        were used. The rapid development, power, and problem of
to collect increasingly data with increasing research produc-      AI and network medicine technology have speed up the
tivity. Leading centers are developing to handle this type of      development of treatment in healthcare. The study is a
cases with large-scale data. They are going to research how        strong proof for the use of AI-assisted tools for drugs and
much accurate equipment is required for the new generation         repayment for human diseases, including COVID-19 in
of biomedical data scientists. In today’s world, NIH and Big       times of fatal disease [11]. Various studies have been consid-
Data to Knowledge (BD2K) seek the position of data sciences        ered for the qualitative analysis of the COVID-19 [12–14].
in biomedical research [4].                                        These studies have mainly considered corona virus from
     Health information technology is designed due to the          different perspectives.
solution of complex problems such as health disparities.                The exposure of corona virus disease 2019 puts an
Mix results can be obtained by demonstrating a direct              increasing burden on the healthcare system. While infected
impact on health outcomes. For all these, we must know             patients have nonserious signs, which can be handled at
about collective intelligence, big data, informatics capacities,   home, some people develop serious symptoms and need to
corresponding terms of smart health, knowledge exchange            be hospitalized. That is why it is important to effectively
knowledge, ecosystem, and situational awareness. Standards,        evaluate the severity of COVID-19 and identify the hospi-
guidelines, and objections are established by public health        tal’s priorities. COVID-19 has been published in a number
informatics for health equity and health disparities, which        of countries in the short term. By choosing a session as a
will increase health literacy and access to care [5]. Fodeh        time of patient discharge, including rescue techniques and
and Zeng [6] have presented the notation of big data which         statistical analysis, it is time to seriously prepare it for deci-
is used for three words: volume, variety, and velocity. This is    sion makers to be prepared for more hospital burden. A cube
the foremost concept which is associated with the dimen-           model was proposed to validate and verify the actual and con-
sion’s volume, and the variety is very relevant to the biomed-     firmed cases and deaths, which was then accepted by the
ical data gained by patients, including tabulated and              World Health Organization. Often during the cholera disease,
structured data. The biomedical research communities are           this research-assisted pairing can be learned. Four diagnostic
struggling to escalate in the clustering and analysis of big       models, including lymphocytes, lactate dehydrogenase,
data. Mining big data probably holds plenty of information         C-reactive proteins, and neutrophils, have been developed
and has a possible way and source for giving knowledge;            and validated using the XGBoost algorithm [15–17].
data mining and machine learning methods are useful for                 The contribution of the proposed research is to review
such data series in a challenging task. Kavakiotis et al. [7]      the existing research associated to eHealth and mHealth
have discussed a lot of data on advance technologies which         where various models are discussed. The approaches along
are feasible for medical persons. Researchers have led a lot       with the uses of big data for diagnosis of disease in health-
of research on diseases like diabetes mellitus, concerning         care system are elaborated. The study summarizes the recent
the different types of datasets that are mainly used. These         promising applications of AI and big data in medical health
hypotheses will lead us about the various knowledge of tech-       and electronic health. The study will possibly add value to
nologies and diseases in the world. One should have suffi-           the research in diagnosis and patient care.
cient data and knowledge about machines and technology                  This paper is structured as follows: Section 2 shows the
before using it, as most of the machines used in medical           related work to the planned research. Details regarding the
and healthcare emit radiation which is quite harmful and           applications of big data and AI are given briefly in Section
dangerous for humans and buildings. The proof of principle         3. Challenges and barriers faced by healthcare are shown in
has been provided for lung cancers which has been collected        Section 4. The conclusion of the paper is given in Section 5.
from five locations. The euroCAT infrastructure has success-
fully implemented as a radiation clinic. Radiation oncology        2. Literature Review
is facilitated as a euroCAT network and will facilitate the
machine [8]. In daily clinical work, imaging data usage has        Miller et al. [18] have presented an overview of biomedical
the ability to improve cancer care by knowing tumor biology        data for the derivation of new knowledge on how clinical
and helping in the implementation of precision medicine.           decision support tools can be developed from the wealth of
For accession of the tumor and its microenvironment, radio-        available data to modernize clinical care and scientific
mics helps in monitoring and evaluating the tumor features         research of diseases related to neurodegeneration in the pre-
such as temporal and spatial heterogeneity.                        cision medicine era. Garcia-Ceja et al. [19] have surveyed
     Limkin et al. [9] have provided a study case which assists    sensor data and machine learning about mental health mon-
in ensuring the actual development and enhancement of              itoring systems. The main focus of the study is about mental
radiomics as a biomarker and facilitating its implementation       disorders or conditions like stress, anxiety, depression, and
in clinical practice. Gebremeskel et al. have proposed a data      bipolar disorder. It is suggested to guide the review of the
mining technique of optimization for envisioning of knowl-         related categorization taxonomy study and present the
Review Article Influential Usage of Big Data and Artificial Intelligence in Healthcare
Computational and Mathematical Methods in Medicine                                                                               3

overall phase of MHMS. Digital health technologies allow          and admirable benefits of this development, it is important
access to optimize clinical care delivery and clinical research   to implement it in a cloud-based environment. Beside
to access the information about age of healthcare. Despite        numerous and elegant benefits of eHealth in cloud comput-
the usage of such technologies in research and clinical care      ing, its full optimization is still obstructed by facing some
faces issues such as data quality, privacy, and regulatory,       challenges like privacy and security. The study mainly
etc. Sharma et al. [20] summarizes think tank meeting             focuses on contemporary literatures of many techniques,
proceedings with goals to delineate a framework for appro-        and the mechanism is used to control privacy and security-
priately using digital health technologies in research and        related matters in eHealth. The robustness and fragility of
healthcare delivery. In Alzheimer’s disease (AD), clinical        these approaches were vocalized. 110 articles were reviewed,
development programs have failed to modify new diseases           and several models were figured out in which solutions were
and to establish quality work instrument for the treatment        adopted in it. These models were compared, and the articles
of new and advanced diseases. The report is based on com-         were reviewed in which a standard and acceptable definition
puter modeling and simulation, which plays a role of being        of eHealth was given as it was recommended. Privacy and
powerful and observational in AD research [21]. Super com-        security requirements were also discussed and provided as
puters are analyzing big data using algorithms of advanced        per the recommendation of the Health Insurance Portability
deep learning machines which have allowed the improve-            and Accountability Act (HIPAA). Finally, a dependable and
ment of output like visual image interpretation to formerly       secured architecture for eHealth was proposed that could
imperceptible levels and promised to change the practice of       guarantee the effective, regulated, and dependable access
medicine. AI is a well-known field about what everyone is          framework to health information [25]. It has also applica-
aware of; it is bringing a tremendous progress in such fields      tions in speech and language therapy. However, these thera-
like automation of clinical decisions, disease detectors, imag-   pies are often less willingly available for a few months after
ing analysis in medical diagnosis classifiers, an interven-        stroke. Palmer et al. [26] have assessed a self-managed com-
tional procedures, and it has the strength to significantly        puterized speech and language therapy (CSLT) aiming to
affect the practice interventional cardiology. This distinctive    deliver more therapy to patients than they can access
nature of interventional cardiology has made it a supreme         through usual care alone. CSLT plus usual care significantly
target for the evolution of artificial intelligence-based          improved in individually relevant word findings, but they
approaches which are delineated to enhance real-time clini-       did not improve in conversation. Schoettler et al. [27] have
cal decision making, the elegant flow of work in laboratories,     presented a review of genome-wide association studies
and standardized catheter-based procedures using advance          (GWASs) of asthma and those related to allergies, in which
robotics. This study highlights the scope of artificial intelli-   he concluded that childhood asthma is correlated to the
gence, its potential applications, and limitations in interven-   most independent locus compared with other specified
tional cardiology [22].                                           groups of allergic and asthma cases, while adult asthma is
    The healthcare domain has obtained its effect by the           correlated to fewer genes which can be considered as a sub-
influence of big data since the sources elaborated in the insti-   set of those similar with childhood asthma.
tutions of healthcare are familiar for their dimensions, heter-
ogenous complication, and excessive spirit. Big data’s            3. Applications of Big Data and AI
analytical techniques, tools, and platforms are perceived
among numerous fields; their effect on healthcare institu-          A lot of data in the world is available in different hospitals
tions for fulfilling novel use cases for possible healthcare       and healthcare centers, which are collected from disease-
applications shows promising research directions. In this         affected humans. The amount of this data is increasing day
circumstance, the success of these applications is completely     by day [28]. Researchers are trying to analyze the data and
dependent on the fundamental architecture and optimiza-           extract the diseases that affect the human body. Pobiruchin
tion of suitable and proper tools as signified by introducing      et al. [29] have extracted new approaches for leveraging can-
research attempts. Palanisamy and Thirunavukarasu [23]            cer in the development of Markov Models, from which the
have discussed many analytical areas that endure in the           researchers easily extract the clinical patient of breast cancer,
patient care system from stockholders’ perspectives and           which can be easily used for freshly affected bodies in the
appraise big data structure with respect to fundamental data      real world. The study has mainly focused on publishing
sources, systematic capabilities, and application fields, while    research on breast cancer, which is concerned with the clin-
the intimation of big data that aids in the development of        ical study of data. In this registry, normally, the patient is
healthcare ecosystem is also discussed. The modern tech-          covered for 8 years from the clinical registry report. Using
nique, hidden Markov model (HMM), can be used to man-             the Markov model is very good for creating a good opportu-
age massive data; the inference of the reproducible and           nity to make a future planning in the existence of data in
interpretable dynamic brain network can be made possible          healthcare and clinical centers. This is useful to get an easy
in different dataset ranges including various other tasks          way to identify the patients and their affections in the future.
[24]. The previous enhancements in ICT have indisputably          A deep rule-based fuzzy system is used for accurate mortal-
improved the quality of people’s lives around the globe.          ity rate prediction in hospital intensive care units (ICU’S). In
Computer technology has led to very needy and tremendous          pursuant to this approach, in each base building unit of
enhancement in the health sector which is commonly                DRBFS, the same system of input has to be kept. In first 48
known as eHealth (electronic health). For optimizing the full     hours in ICU, with heterogeneous featuring a set of
Review Article Influential Usage of Big Data and Artificial Intelligence in Healthcare
4                                                                    Computational and Mathematical Methods in Medicine

admissions were extracted for mortality rate in hospital [30].    used by health professionals, this paper is aimed at regener-
With data concerned to the health records, recurrent neural       ating a new growth chart for French children. 32 random
networks were used for disease risk prediction. The model         primary care pediatricians and 10 volunteer general practi-
results were better for small datasets, whereas for large-        tioners were selected across the French main regions where
scale ones, the concerned model and its application to differ-     the electronic medical record software was used. All data
ent patients across the population throughout hospitals were      of physical growth was extracted for pediatric patients with
not evaluated [31]. Epidemiologist is using electronic health     anonymization. A new growth chart was derived which
records which assist evidence provision for interval guide-       was then compared with the WHO chart and found it
lines and screening intervals while censoring interval and        correct and error free [41].
undiagnosed sickness make a path to issues like substantial           Mechanical therapies are primarily prescribed for motor,
analytic risk estimation which cannot be addressed by using       duodenal, endocrine, urinary, respiratory, digestive, neural,
Kaplan-Meier methods [32]. Zhao et al. [33] have deter-           and cardiovascular diseases. The speedy evolution of health-
mined the illustration of temporal data in electronic health      care is driven by the enhancement of technologies related to
record (EHR); there were several methods based on sym-            the Olympics, biomedical, and scientific sciences. Appropri-
bolic sequence representation of time series data, which          ate medicine has been designed to implement specific and
can be used in several ways, while several evidence relies        more specific treatments for specific diseases and patients
on the standard of symbolic sequence representation by            [42]. The healthcare industry was booming in the pharma-
comparing it to particular orders initiated by clinical experts   ceutical industry. Once again, like the confirmations, text,
with the use of domain knowledge. In various other                numbers, photos, and catalogs, any information that lists
applications of big data based on trajectory data, the regular    the large and irregular discovery of various health service
behavior of private cars is extracted [34–36].                    management systems lists patient information. At the same
                                                                  time, hospitals were part of several geographical depart-
3.1. Disease Prediction and Big Data. Big data and artificial      ments in different locations. In different ways, fact provide
intelligence approaches are commonly presented in mHealth         patient health information from time to time. In response
and eHealth applications for self-management and home             to these problems, Damien is a one-sided solution to health-
care of many long-term health conditions. Adaptation of           care interventions for a marine, vegan-based healthcare
these applications for self-management in asthma multifac-        management system [43]. The incidence of pediatric and
torial persistent diseases needs validation and evaluation in     pediatric diseases such as pneumonia and diarrhea are glob-
real-life setups along with the enhancement of the patient        ally challenging. The Patient Demographic and Health Sur-
level to personalize predictions concerning the control status    vey (DHS) deportation post from India once again created
of asthma and increasing pitfalls. The study presents a short-    cesarean section (CBNs) of the day and explored the loca-
term prophecy technique for controlling the asthma status,        tion factors of childhood diarrhea. Location of freeware tools
where multiple categorization models are considered trains        for DHS data, graphic structure learning with world score-
for each observed framework along with obligatory prepro-         based limitation, and hybrid structure learning algorithms
cessing techniques to improve strength and effectiveness.          are described. The study identified the outcome of missing
The support vector machine, random forests, Bayesian net-         values, sample size, and constraints of knowledge based on
work, and AdaBoost are considered machine learning algo-          each of the structure learning algorithms and evaluates their
rithms used in this review [37]. Xin-Di et al. [38] have          accuracy with multiple scoring functions. [44]. Waring et al.
composed 252 formulae from the synopsis of prescriptions          [45] have helped healthcare specialists in the knowledge lit-
of the Golden Chamber and a treatise on exogenous febrile         erature review in the Greater East using automated machine
disease, looking to identify the basic rules dominating the       learning (AutoML) with the latest experience of machine
choosing of herbal mixtures by probability models and big         learning models “O-Shelf” with limited information data
data technology. Pashazadeh and Navimipour [39] have              specialization. The possible opportunities and risk of using
provided a detailed and systematic review of the modern           AutoML in healthcare in the field are also located in the
mechanisms related to healthcare applications in big data         foreground, as well as the demands of AutoML in healthcare
fields. Five of the discussed categories are heuristic based,      in response to the attack. The use of ML techniques has the
hybrid mechanism, machine learning, cloud based, and              possibility to improve outcomes of health, reduce costs of
agent based. Harous et al. [40] have enlightened the applica-     healthcare, and demonstrate clinical research. The Greatest
tions of big data for obesity management using sensory and        Conventions Nowadays, using AI, previously discovered that
social data in mobile health architecture, which are mainly       knowledge can be found in large collections. Clinical oncol-
proposed for assessing, detecting, and controlling obesity,       ogy data is a start to clinical practice and research with a
which depend on a mobile phone to be used for complex             wealth of multithreaded documents and metadata. Most of
operations and processing of the data collected. Using this       our clients include companies and employees. Inadequate
newly developed mobile architecture, one can tackle the           interventions, short-term stay, and vision can improve
challenges of obesity control, monitoring, and prevention.        patient care. Incorporating powerful AI algorithms has been
Different algorithms have been used to generate featured           instrumental in changing the presentation and common
suggestions, recommendations, and warnings to control             sense of high-intensity radiation oncology. However, this is
obesity and its related diseases. In the context of large data-   possible by establishing a clinical scientific community with
sets of physical growth measurements which are regularly          radiation oncology [46].
Review Article Influential Usage of Big Data and Artificial Intelligence in Healthcare
Computational and Mathematical Methods in Medicine                                                                             5

3.2. Technological Innovations in Healthcare. Blockchain         generating these values, analysts gather values for a precho-
technology, such as the vascularization of inherited traits,     sen health state from a console who represents the public
router transparency, and identification information, was          and then use a mathematical algorithm to produce values
introduced in 2008 in the Bitcoin cryptocurrency. A wide         for 243 states. To estimate the rationality and correctness
range of domain-level domains engaged in the health              of both the newer criteria and, earlier, in terms of forecasting
domain. The purpose of this study is to systematically           of values for all health states and of the values of common
acquire startup publications and evaluate the Turkish posi-      health states in specific, preexisting dataset has been used
tion of the use/offer of blockchain to improve processes          that accommodates VAS (visual analogue scale) values from
and services in healthcare, health sciences, and services san-   126 students while each student has valued all 243 states. A
itary [47]. There are some biological processes in which cell    sequence of representations was produced, and eventually,
regulation and signaling are involved through the assembly       data were designed according to each model. Few of them
of protein complexes. When there is any change occurring         were used in the past [52]. Related to scientific activity, this
through protein-protein interfaces, it will affect the forma-     study shows a large data depository, containing epidemiol-
tion of multiprotein complexes. Due to this, sudden change       ogy, clinical, and molecular data, which offers research
occurs in the cell functions and the development of genetic      opportunities and is essential to assist scientific activity.
diseases occurs like cancer and develops drug resistance.        The advantages of massive data may contain low-cost collec-
These proteins are essential and lead to the formation of        tion. Restrictions may contain the cost and difficulty of data
many resources for predicting their effects. It should prepare    storage and processing, which is essential for modern
for how mutations affect the structure and body of multipro-      approaches like formatting, analysis, and concerns about
tein complexes [48]. There are some models under                 security, accuracy, and reliability. It is explained that the
development for the solution of two different problems com-       source of big data and tools for its analysis can be used to
putationally, to allocate the fund for infectious diseases in    help and diagnose the treatment and management of derma-
the deserts and forests of Africa. Guidance about empirical      tologic diseases [53]. Diabetes is a condition in which glu-
data collection and theory development for the development       cose levels are lowered into the bloodstream with a slight
in the future is provided [49]. Biomedical research is getting   delay, causing the inability to metabolize it. When the insu-
completely changed with big data. The extraordinary              lin in the body does not respond properly or if the body does
advances of automatic data collection of large-scale clinical    not produce it, this results in diabetes. Serious and long-term
and molecular data have caused prime challenges to data          health issues are reported. The administration does not
simplification and analysis, summarized for the evolution         really treat electives that include heart pain, lung disorders,
of new computational techniques. The development of the          skin and liver disorders, nerve damage, and noise damage;
system with the efficient use of biomedical big data in            quick findings are rare. Thakkar et al. [54] has discussed
particular medicine will need noble scientific and technical      some valued techniques to excavate in the future and for
creation in which engineering, infrastructure, financial man-     the diagnosis of diabetes.
agement, and projects will be included. In this study, how            Tandon et al. [55] present current issues and opportuni-
the transformation of data-driven techniques provides the        ties to use blockchain technology to improve health perfor-
probability to address many of these problems has been           mance. The SLR (systematic literature review) results show
reviewed, directing the elaboration of the theory on system      that blockchain is being used to create new jobs to improve
functions and the generation of technical models and             the standards for the management, distribution, and pro-
assisting the design of clinical procedures in precision med-    cessing of personal medical records. Implementing the
icine [50].                                                      blockchain technology is part of the healthcare system, at
    Ear disease and mastoid diseases can be easily handled if    verity in the healthcare industry, where the significant value
early detected with appropriate medical care. However,           has been added by improving the performance, nearness,
sometimes, we do not have specialists or we may have com-        advancement of technology, protection, security, and pri-
paratively low diagnostic precision if a new way of diagnostic   vacy of information management systems. The collection
policy is needed, in which deep machine learning can play a      and elaboration of information on public technology has
noble role. Cha et al. [51] have presented a machine learning    been incorporated by the government and by the real estate
model for automatically diagnosing ear disease with the help     companies of the whole world as a strategic solution to
of datasets having a total of 10544 images which were            alleviate the information of COVID-19 that prevents infor-
trained using CNN to categorize internal eardrum and exter-      mation. However, the law regarding the end-of-life monitor-
nal auditory canal features into 6 categories of ear diseases,   ing system to monitor those who are unable to read the
which has covered most of the ear diseases.. Inception-V3        following documents for the help in using the documents
and ResNet101 are the learning models which have been            of power of the public in the world. EHRs contain valuable
chosen for the time training and accuracy, which has an          information on the progression of Rangatahi’s disease and
average accuracy of 93.67%. This classifier is trained in var-    the tragedy of treatment. In the heart failure study, Esther
ious conditions and environments and gives better results of     and her colleagues proposed a scheme for extracting short-
diagnosis, so it is more compatible for the use in a practical   term sequential samples from CCE data that grading
environment. For various countries, EQ-5D-3L (three-level        improves phenotype and prediction and is also professional.
EuroQol five-dimensional questionnaire) value sets are            Brief information on EHR on disease progression and treat-
entrenched to evaluate the utility of the health state. Before   ment outcomes are given. This article presents a method for
6                                                                    Computational and Mathematical Methods in Medicine

representing transient transitional CCE observations up to       ations and poor replication of genetic associations. Asthma
machine learning. Using clinical data from patients with         is not a single disease but a group of similar diseases with
heart disease, EHR drug transfer and provisional representa-     some clinical manifestations. Big data for the disease is not
tion from diagnostic and class records for prescribing activ-    providing a solution, just giving information about the dis-
ities are given [56]. Virtualization (AI) and machine learning   ease. Bayesian and frequent approaches make us understand
are having a real impact in many areas of the healthcare         about the ethnology of diseases commonly used in statistical
industry. The cancer site is a global phenomenon. Efforts         machine learning and can be applied to both big and small
to reduce the chances of accidents should be timely. How-        data in healthcare research. We need to know about the real-
ever, metastatic development, serotonin frequency, and drug      ity of asthma and not to just give predictions only. We have
inhibition are observed. It is important to find new bio-         to get awareness from methodological polemics on data sci-
markers that stimulate group resistance and improve drug         ence. The Bayesian and frequent paradigms of the present
delivery to improve carriers to improve carriers [57]. Time-     artificial dichotomies are given. Big data are required to
line risk assessment tools for heart failure use a database      divide the asthma into subtypes to get the solution. Exploit-
designed with realistic, one-time clinical document scans        ing disaggregated phenotypes and genomic research and the
and low accuracy. The purpose of this study was to create        exploration of lung function as an intermediate phenotype.
a comprehensive 30-day plan for unannounced announce-            Cohering inductive and deductive statistical approaches to
ments and accurate forecasts for all. All-cause mortality        epidemiology pragmatic bayes and unifying graphs are pre-
(ACM) connects clinical and clinical information available       sented [65]. There are techniques and methods by which
on the electronic medical record system. There are three pre-    patients can be classified effectively as those who have
dictive indicators for a 30-day unstructured reading or ACM      asthma and those who do not have those in long-term
created using a step-by-step method: (a) index admission         remission [66]. VA-PODR (Veterinary Affairs Accurate
model, (b) feature-aggregated model, and (c) index dis-          Oncology Data Collection) is a worldwide large repository
charge model [58]. The study has talked about artificial          of well-known data concerned with diagnosed patients of
intelligence that it has the ability to mimic human character-   cancer in VA’s (Veterans Affairs) department. This data
istics, knowledge storage, learning capacity, and so on. There   consists of long-term clinical data from the nationwide Vet-
are a majority of techniques which have been already applied     erans Affairs eHealth record, medical photography data,
in medicine and healthcare, such as the C-statistic model,       tumor sequencing targets, pathology slides, and the Veterans
which was used in North Carolina hospitals as the finest          Affairs central cancer registry as well as CT (computer
model for the prediction and enhancement of the efficiency         tomography) scans. A subset of this data storage is available
of preventive medicine [59]. Table 1 represents some of the      in GDC (genomic data commune) and the TCIA (The
existing research work available in the literature.              Cancer Imaging Archive) [67].
                                                                      The allergic conjunctivitis population has a frequency of
3.3. Cardiovascular. Cardiovascular imaging is getting chan-     15–20%. However, due to the lack of understanding, the
ged with the rise of data science. There are many techniques     multicause of chronic obstructive pulmonary disease is that
which have been applied in cardiovascular medicine for           the environment, residence, and direct distribution have
exploring different types of diseases, as well as to improve      complex relationships. The benefits of medicine for chronic
patient care quality and mortality rate. The potential of AI     kidney disease patients and the type of treatment for demen-
in cardiovascular medicine is tremendous, although igno-         tia, as well as the possibilities for other diseases that need to
rance of the challenges may overshadow its potential clinical    be known by patients that will help patients according to the
impact [60]. There are some complexities like efficiency,          symptoms and risk factors of the intervention’s social
timing, and missing diagnoses which occur in all stages of       empowerment and rehabilitation of people with chronic
the imaging chain. The dependency of AI’s applications falls     obstructive pulmonary disease to reduce the body helping
on vigorous data, suitable computational techniques, and         to create permanent solutions and prevent the escalation of
tools. While in some areas, it depends on the validation of      social ills [68].
its clinical application to image sectionalization, automated         In biomedical repositories, the size of somatic genomes
measurements, and diagnosis. Cardiovascular disease has          is getting increase. So predicting documents related to can-
now accurate imaging combined with big data from the             cer sets using mathematical algorithms are described. Min-
eHealth record and pathology to better characterize the dis-     ing models of conventional gene-based somatic cancer are
ease and personalized therapy [61]. Research has explored        unhampered by somatic gene ranking and feature extraction
the area and presented studies on the available literature in    due to high computational cost and memory of large data-
the area [62, 63].                                               sets. A wide range of characteristics, preferences, and feature
                                                                 squeezing procedures is available, and they are normally
3.4. Big Data and Disease Identification. In daily clinical       deployed in many areas. Each of these procedures tries to
work, imaging data usage has the ability to improve cancer       extract and squeeze some new irrelevant characteristic fea-
care by knowing tumor biology and helping in the imple-          tures from the trained datasets, having a goal of increasingly
mentation of precision medicine. For accession of tumor          accurate results for the newly arranged documented data.
and its microenvironment, radiomics helps in monitoring          Extraction or squeezing of data is an activity of delivering
and evaluating tumor features such as temporal and spatial       required and related data from a large collection of datasets
heterogeneity [64]. Asthma is inconsistent in clinical associ-   according to the given information. There are criteria for
Computational and Mathematical Methods in Medicine                                                                               7

                                               Table 1: Existing research in the area.

S. no.                                                            Title                                                       Year
1                       Modeling future price and diffusion in health technology assessments of medical devices                2016
2                                                  Big data effort in radiation oncology                                       2016
3                                  Processing and analyzing healthcare big data on cloud computing                            2016
4                                            Decision rules for health system strengthening                                   2016
5                                              Use and analysis of big data in dermatology                                    2017
6                                                AI in precision cardiovascular medicine                                      2017
7                                                         Disaggregating asthma                                               2017
8                            Predicting the risk of acute care readmissions among rehabilitation inpatients                   2018
9                                        Selecting health states for EQ-5D-3L valuation studies                               2018
10                                        Modeling asynchronous event sequences with RNNs                                     2018
11                Map reduce-based hybrid NBC-TFIDF algorithm to mine the public sentiment on diabetes mellitus               2018
12                                            The trifecta of precision care in heart failure                                 2018
            Roadmap for innovation-ACC health policy statement on healthcare transformation in the era of digital health,
13                                                                                                                            2018
                                                      big data, and precision health
14                              Authenticating health activity data using distributed ledger technologies                     2018
15                                      Spread of health-related misinformation on social media                               2019
16                                             Big data analytics for personalized medicine                                   2019
17                                                       Diagnosis of ear disease                                             2019
18                                                     AI in cardiovascular imaging                                           2019
19                                                 Big data visualization in cardiology                                       2019
20                                     Big data features, applications, and analytics in cardiology                           2019
21                      Transitive sequencing medical records for mining predictive and interpretable temporal                2020
22                                         AI and big data in cancer and precision oncology                                   2020
             Electronic health data and machine learning for the prediction of 30-day unplanned readmission or all-cause
23                                                                                                                            2020
                                                         mortality in heart failure
24                                        The veteran affair precision oncology data repository                                2020
25                                     Medical big data for P4 medicine on allergic conjunctivitis                            2020
26                          Somatic cancer gene-based biomedical document feature ranking and clustering                      2019
27                             Anatomization of data mining and fuzzy logic used in diabetes prognosis                        2020
28                                                       Blockchain in healthcare                                             2020

ranking the information which is sorted as the best results           in fields related to health, where it may cause to detain or
will have the top priority so it will be on the top of the pro-       stop effective care, while in some cases, it is menacing the
vided list. Experimental results have different cluster sizes          lives of individuals. Wang et al. [71] have exposed the pres-
with different gene features for clustering of somatic docu-           ent proof and made it clear in an understandable way; the
ments. And their generated results prove that the existing            mechanism of misinformation spreads. The most substantial
model has a high quality of computational clustering [69].            and considerable topics include misinformation related to
                                                                      vaccination, Zika virus, and Ebola, even though others, such
4. Challenges and Barriers Faced by Healthcare                        as fluoridation of water, nutrition, cancer, and smoking are
                                                                      also highlighted. Most studies employed content analysis,
The healthcare organizations are facing many challenges like          social network analysis, or experiments, drawing on dispa-
analyzing large-scale data. With the rapid increase in health-        rate disciplinary paradigms. Respective systems are used to
care applications, many devices are used to generate verities         classify this data based on the result and condition of each
of data and many of them are used for better analyzing data           sentence [72]. Future research should examine the suscepti-
and for better decision making [70]. The following are the            bility of different sociodemographic groups for misinforma-
challenges which todays healthcare organizations are facing.          tion and to understand the role of belief systems on the
                                                                      intention to spread misinformation.
4.1. Social Media. Analysts of today’s era are giving name to
the period that we are living in “an era of fake news” based          4.2. Knowledge about Machines and Technology. Most of the
on the spread of misinformation produced, which may be                technicians or users of the machines do not have enough
spread intentionally or unintentionally. As it is affecting all        knowledge on using the technologies/machines [73]. To
areas and fields of life, it also creates problems and issues          overcome this problem, technicians and operators should
8                                                                Computational and Mathematical Methods in Medicine

    Article          Number                 Discipline
                                                                     Paper
                                                                                     Location of conference   Total

              Figure 1: Details of the search process in the Springer library from various perspectives.

                            Figure 2: Details of the search process in the ACM library.
Computational and Mathematical Methods in Medicine                                                                                               9

     Article                              Publisher
                                                             Paper
                        Number
                                                                          Location of conference   Total       Publication topic         R.Pap

                                        Figure 3: Details of the IEEE library from various perspectives.

               Publication title      Pulications          Subject area              Paper
                                                                                                           Access                  R.P

                         Figure 4: Details of the search process from various perspectives in the ScienceDirect library.

have sufficient data and knowledge about machines and                          ful and dangerous for humans and buildings. The euroCAT
technology before using it, as most of the machines used in                  infrastructure needs to be implemented as a radiation
medical and healthcare emit radiation which is quite harm-                   clinic [8].
10                                                                      Computational and Mathematical Methods in Medicine

                                          Figure 5: Details of the ScienceDirect library.

4.3. High-Power Consuming Technologies. As these artificial          in health may be significant in identifying diseases on the
machines can perform a complex tasks; many of the technol-          next level. There are some things which need to be observed
ogies operate on large scale in large areas for a long time,        before transforming shifting from traditional healthcare to vir-
which consumes much electric power while there is a big             tual and real-time diagnoses which remains lacking in true
issue of power in most of the developing countries which            evaluation whether these inventions are really improving and
needs to be solved and adjusted to give plenty of facilities        overcoming the healthcare quality or not. If any of the new
in healthcare.                                                      technology is adopted which is not based on evidenced care
                                                                    may risk unintended outcomes like privacy breach, increase
4.4. Expensive. This is the generation of robotics and artifi-       in care cost, etc [75]. Distributed ledger technology needs to
cial intelligence which has large significance in the field of        be implemented to ensure the integrity and authenticity of
healthcare. However, technologies, machines, and data stor-         data produced by embedded systems [76].
ages, either cloud database or our own database, are mostly             The existing literature was studied to know about the
not affordable by the organizations because of high prices.          details of research available in the area of healthcare based
By using single-cohort model parameters, we can obtuse              on AI and big data. Figure 1 represents the details of the lit-
the price issues which we are facing and we will face in the        erature in the Springer library from various perspectives as
future [74]; this will add a significant value to the medical        given in the figure.
organizations as well as patients in rural areas who cannot             Figure 2 elaborates the details of the search process in
afford and do not have access to these technologies.                 the library of ACM.
                                                                        The library of IEEE was also searched for getting the
4.5. Transformation. A healthcare transformation is a               associated details. Figure 3 describes the details of the search
method of sharing thoughts of a company to work on that.            process for the IEEE library.
It has the importance as the old technology is transforming             Lastly, the ScienceDirect library was tried and the details of
to a newly developed and evolving technology based on               the library from various perspectives are shown in Figure 4.
innovation, which may include digital health, eHealth,                  This library was further elaborated and some of the
mHealth, and sensor-based technologies. Such enhancement            details are shown in Figure 5.
Computational and Mathematical Methods in Medicine                                                                                           11

5. Conclusion                                                                  mining methods in diabetes research,” Computational and
                                                                               Structural Biotechnology Journal, vol. 15, pp. 104–116, 2017.
Big data and AI have revolutionized everything that has                  [8]   T. M. Deist, A. Jochems, J. van Soest et al., “Infrastructure and
brought comfort to people’s life in all aspects of daily life.                 distributed learning methodology for privacy-preserving
Especially, it is bringing innovative technologies in health-                  multi-centric rapid learning health care: euroCAT,” Clinical
care systems. It has transformed the conventional and tradi-                   and Translational Radiation Oncology, vol. 4, pp. 24–31, 2017.
tional health system to a technological systems in the form              [9]   E. J. Limkin, R. Sun, L. Dercle et al., “Promises and challenges
of cardiovascular, oncology, ear, asthmatic, allergic, cancer,                 for the implementation of computational medical imaging
and or any other diseases. Big data has made it easy to                        (radiomics) in oncology,” Annals of Oncology, vol. 28, no. 6,
diagnose disease through virtual and real-time systems.                        pp. 1191–1206, 2017.
The contribution of this paper is to review the present                 [10]   G. Belay Gebremeskel, B. Hailu, and B. Biazen, “Architecture
research applicable to mHealth and eHealth where various                       and optimization of data mining modeling for visualization
approaches and models are discussed which use big data                         of knowledge extraction: patient safety care,” Journal of King
for diagnosis and healthcare system. This paper has summa-                     Saud University-Computer and Information Sciences, 2019.
rized the current encouraging applications of AI and big                [11]   Y. Zhou, F. Wang, J. Tang, R. Nussinov, and F. Cheng, “Artifi-
data in medical health and electronic health, which have                       cial intelligence in COVID-19 drug repurposing,” The Lancet
potentially added value to the diagnosis of disease and                        Digital Health, vol. 2, no. 12, pp. e667–e676, 2020.
patient care. The proposed research will help researchers to            [12]   K. Shah, T. Abdeljawad, I. Mahariq, and F. Jarad, “Qualitative
devise new solutions in the area of healthcare.                                analysis of a mathematical model in the time of COVID-19,”
                                                                               BioMed Research International, vol. 2020, Article ID
                                                                               5098598, 11 pages, 2020.
Conflicts of Interest                                                   [13]   I. Arpaci, S. Alshehabi, M. al-Emran et al., “Analysis of Twitter
                                                                               data using evolutionary clustering during the COVID-19 pan-
The authors declare no conflict of interest.
                                                                               demic,” Computers, Materials & Continua, vol. 65, no. 1,
                                                                               pp. 193–204, 2020.
Acknowledgments                                                         [14]   H. Alrabaiah, M. Arfan, K. Shah, I. Mahariq, and A. Ullah, “A
                                                                               comparative study of spreading of novel corona virus disease
This research was financially supported by the Humanities                       by ussing fractional order modified SEIR model,” Alexandria
and Social Science Fund of the Education Department of                         Engineering Journal, vol. 60, no. 1, pp. 573–585, 2021.
Henan Province in 2020 (Grant number: 2021-ZZJH-233),                   [15]   Y. Zheng, Y. Zhu, M. Ji et al., “A learning-based model to eval-
Luoyang Humanities and Social Science Fund in 2021                             uate hospitalization priority in COVID-19 pandemics,” Pat-
(YB-153), provincial and ministerial preresearch projects in                   terns, vol. 1, no. 6, article 100092, 2020.
Luoyang Institute of Technology and Science of 2019, and                [16]   M. Nemati, J. Ansary, and N. Nemati, “Machine-learning
the general grant project of the Provincial Social Science                     approaches in COVID-19 survival analysis and discharge-
Achievements Review Committee in 2021 (Grant number:                           time likelihood prediction using clinical data,” Patterns,
XSP21YBZ077).                                                                  vol. 1, no. 5, article 100074, 2020.
                                                                        [17]   H. T. Likassa, W. Xain, X. Tang, and G. Gobebo, “Predictive
References                                                                     models on COVID 19: what Africans should do?,” Infectious
                                                                               Disease Modelling, vol. 6, pp. 302–312, 2021.
 [1] S. M. Willems, S. Abeln, K. A. Feenstra et al., “The potential     [18]   J. B. Miller, G. Shan, J. Lombardo, and G. Jimenez-Maggoria,
     use of big data in oncology,” Oral Oncology, vol. 98, pp. 8–              “Biomedical informatics applications for precision manage-
     12, 2019.                                                                 ment of neurodegenerative diseases,” Alzheimer's & Dementia:
 [2] D. Bzdok and B. T. T. Yeo, “Inference in the age of big data:             Translational Research & Clinical Interventions, vol. 4, no. 1,
     future perspectives on neuroscience,” Neuroimage, vol. 155,               pp. 357–365, 2018.
     pp. 549–564, 2017.                                                 [19]   E. Garcia-Ceja, M. Riegler, T. Nordgreen, P. Jakobsen, K. J.
 [3] C. Garcia-Vidal, G. Sanjuan, P. Puerta-Alcalde, E. Moreno-                Oedegaard, and J. Tørresen, “Mental health monitoring with
     Garcia, and A. Soriano, “Artificial intelligence to support                multimodal sensing and machine learning: a survey,” Perva-
     clinical decision-making processes,” EBioMedicine, vol. 46,               sive and Mobile Computing, vol. 51, pp. 1–26, 2018.
     pp. 27–29, 2019.                                                   [20]   A. Sharma, R. A. Harrington, M. B. McClellan et al., “Using
 [4] A. A. T. Bui, J. van Horn, and NIH BD2K Centers Consortium,               digital health technology to better generate evidence and
     “Envisioning the future of 'big data' biomedicine,” Journal of            deliver evidence-based care,” Journal of the American College
     Biomedical Informatics, vol. 69, pp. 115–117, 2017.                       of Cardiology, vol. 71, no. 23, pp. 2680–2690, 2018.
 [5] T. J. Carney and A. Y. Kong, “Leveraging health informatics to     [21]   H. Geerts, P. A. Dacks, V. Devanarayan et al., “Big data to
     foster a smart systems response to health disparities and health          smart data in Alzheimer’s disease: the brain health modeling
     equity challenges,” Journal of Biomedical Informatics, vol. 68,           initiative to foster actionable knowledge,” Alzheimer's &
     pp. 184–189, 2017.                                                        Dementia, vol. 12, no. 9, pp. 1014–1021, 2016.
 [6] S. Fodeh and Q. Zeng, “Mining Big Data in biomedicine and          [22]   P. Sardar, J. D. Abbott, A. Kundu, H. D. Aronow, J. F. Granada,
     health care,” Journal of Biomedical Informatics, vol. 63,                 and J. Giri, “Impact of artificial intelligence on interventional
     pp. 400–403, 2016.                                                        cardiology: from decision- making aid to advanced interven-
 [7] I. Kavakiotis, O. Tsave, A. Salifoglou, N. Maglaveras,                    tional procedure assistance,” JACC: Cardiovascular Interven-
     I. Vlahavas, and I. Chouvarda, “Machine learning and data                 tions, vol. 12, no. 14, pp. 1293–1303, 2019.
12                                                                               Computational and Mathematical Methods in Medicine

[23] V. Palanisamy and R. Thirunavukarasu, “Implications of big                  ing asthma self- management,” Electronic Notes in Theoretical
     data analytics in developing healthcare frameworks - a                      Computer Science, vol. 343, pp. 3–17, 2019.
     review,” Journal of King Saud University-Computer and Infor-         [38]   H. Xin-Di, D. Chang-Song, L. Hao, X. Shi-Wei, and L. Li-Song,
     mation Sciences, vol. 31, no. 4, pp. 415–425, 2019.                         “Research on herb pairs of classical formulae of ZHANG
[24] D. Vidaurre, R. Abeysuriya, R. Becker et al., “Discovering                  Zhong-Jing using big data technology,” Digital Chinese Medi-
     dynamic brain networks from big data in rest and task,” Neu-                cine, vol. 2, no. 4, pp. 195–206, 2019.
     roimage, vol. 180, no. Part B, pp. 646–656, 2018.                    [39]   A. Pashazadeh and N. J. Navimipour, “Big data handling
[25] N. A. Azeez and C. V. der Vyver, “Security and privacy issues               mechanisms in the healthcare applications: a comprehensive
     in e-health cloud-based system: a comprehensive content anal-               and systematic literature review,” Journal of Biomedical Infor-
     ysis,” Egyptian Informatics Journal, vol. 20, no. 2, pp. 97–108,            matics, vol. 82, pp. 47–62, 2018.
     2019.                                                                [40]   S. Harous, M. El Menshawy, M. A. Serhani, and A. Benharref,
[26] R. Palmer, M. Dimairo, C. Cooper et al., “Self-managed, com-                “Mobile health architecture for obesity management using
     puterised speech and language therapy for patients with                     sensory and social data,” Informatics in Medicine Unlocked,
     chronic aphasia post-stroke compared with usual care or                     vol. 10, pp. 27–44, 2018.
     attention control (Big CACTUS): a multicentre, single-               [41]   B. Heude, P. Scherdel, A. Werner et al., “A big-data approach
     blinded, randomised controlled trial,” The Lancet Neurology,                to producing descriptive anthropometric references: a feasibil-
     vol. 18, no. 9, pp. 821–833, 2019.                                          ity and validation study of paediatric growth charts,” The Lan-
[27] N. Schoettler, E. Rodriguez, S. Weidinger, and C. Ober,                     cet Digital Health, vol. 1, no. 8, pp. e413–e423, 2019.
     “Advances in asthma and allergic disease genetics: is bigger         [42]   C. Song, Y. Kong, L. Huang, H. Luo, and X. Zhu, “Big data-
     always better?,” Journal of Allergy and Clinical Immunology,                driven precision medicine: Starting the custom-made era of
     vol. 144, no. 6, pp. 1495–1506, 2019.                                       iatrology,” Biomedicine & Pharmacotherapy, vol. 129, article
[28] T. Li, Z. Xiao, H. M. Georges, Z. Luo, and D. Wang, “Perfor-                110445, 2020.
     mance analysis of co-and cross-tier device-to-device commu-          [43]   K. A. Shakil, F. J. Zareen, M. Alam, and S. Jabin, “BAMHealth-
     nication underlaying macro-small cell wireless networks,” J                 Cloud: a biometric authentication and data management sys-
     KSII Transactions on Internet Information Systems, vol. 10,                 tem for healthcare data in cloud,” Journal of King Saud
     no. 4, pp. 1481–1500, 2016.                                                 University-Computer and Information Sciences, vol. 32, no. 1,
[29] M. Pobiruchin, S. Bochum, U. M. Martens, M. Kieser, and                     pp. 57–64, 2020.
     W. Schramm, “A method for using real world data in breast            [44]   N. K. Kitson and A. C. Constantinou, “Learning Bayesian net-
     cancer modeling,” Journal of Biomedical Informatics, vol. 60,               works from demographic and health survey data,” Journal of
     pp. 385–394, 2016.                                                          Biomedical Informatics, vol. 113, article 103588, 2021.
[30] R. Davoodi and M. H. Moradi, “Mortality prediction in inten-         [45]   J. Waring, C. Lindvall, and R. Umeton, “Automated machine
     sive care units (ICUs) using a deep rule-based fuzzy classifier,”            learning: review of the state-of-the-art and opportunities for
     Journal of Biomedical Informatics, vol. 79, pp. 48–59, 2018.                healthcare,” Artificial Intelligence in Medicine, vol. 104, article
[31] L. Rasmy, Y. Wu, N. Wang et al., “A study of generalizability of            101822, 2020.
     recurrent neural network-based predictive models for heart           [46]   J. Kazmierska, A. Hope, E. Spezi et al., “From multisource data
     failure onset risk using a large and heterogeneous EHR data                 to clinical decision aids in radiation oncology: the need for a
     set,” Journal of Biomedical Informatics, vol. 84, pp. 11–16,                clinical data science community,” Radiotherapy and Oncology,
     2018.                                                                       vol. 153, pp. 43–54, 2020.
[32] R. Landy, L. C. Cheung, M. Schiffman et al., “Challenges in risk      [47]   A. Hasselgren, K. Kralevska, D. Gligoroski, S. A. Pedersen, and
     estimation using routinely collected clinical data: the example             A. Faxvaag, “Blockchain in healthcare and health sciences-a
     of estimating cervical cancer risks from electronic health-                 scoping review,” International Journal of Medical Informatics,
     records,” Preventive Medicine, vol. 111, pp. 429–435, 2018.                 vol. 134, article 104040, 2020.
[33] J. Zhao, P. Papapetrou, L. Asker, and H. Bostrom, “Learning          [48]   H. C. Jubb, A. P. Pandurangan, M. A. Turner, B. Ochoa-Mon-
     from heterogeneous temporal data in electronic health                       tano, T. L. Blundell, and D. B. Ascher, “Mutations at protein-
     records,” Journal of Biomedical Informatics, vol. 65, pp. 105–              protein interfaces: small changes over big surfaces have large
     119, 2017.                                                                  impacts on human health,” Progress in Biophysics and Molecu-
[34] Z. Xiao, S. Xu, T. Li et al., “On extracting regular travel behav-          lar Biology, vol. 128, pp. 3–13, 2017.
     ior of private cars based on trajectory data analysis,” IEEE         [49]   A. Morton, R. Thomas, and P. C. Smith, “Decision rules for
     Transactions on Vehicular Technology, vol. 69, no. 12,                      allocation of finances to health systems strengthening,” Jour-
     pp. 14537–14549, 2020.                                                      nal of Health Economics, vol. 49, pp. 97–108, 2016.
[35] Y. Huang, Z. Xiao, D. Wang, H. Jiang, and D. Wu, “Exploring          [50]   D. Cirillo and A. Valencia, “Big data analytics for personalized
     individual travel patterns across private car trajectory data,”             medicine,” Current Opinion in Biotechnology, vol. 58, pp. 161–
     IEEE Transactions on Intelligent Transportation Systems,                    167, 2019.
     vol. 21, no. 12, pp. 5036–5050, 2020.                                [51]   D. Cha, C. Pae, S. B. Seong, J. Y. Choi, and H. J. Park, “Auto-
[36] Y. Huang, Z. Xiao, X. Yu, D. Wang, V. Havyarimana, and                      mated diagnosis of ear disease using ensemble deep learning
     J. Bai, “Road network construction with complex intersections               with a big otoendoscopy image database,” EBioMedicine,
     based on sparsely sampled private car trajectory data,” ACM                 vol. 45, pp. 606–614, 2019.
     Transactions on Knowledge Discovery from Data (TKDD),                [52]   Z. Yang, N. Luo, G. Bonsel, J. Busschbach, and E. Stolk,
     vol. 13, no. 3, pp. 1–28, 2019.                                             “Selecting health states for EQ-5D-3L valuation studies: statis-
[37] O. Kocsis, A. Lalos, G. Arvanitis, and K. Moustakas, “Multi-                tical considerations matter,” Value Health, vol. 21, no. 4,
     model short-term prediction schema for mHealth empower-                     pp. 456–461, 2018.
You can also read