Review Article Influential Usage of Big Data and Artificial Intelligence in Healthcare
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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
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
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
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].
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 for6 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 forComputational 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 should8 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.
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research applicable to mHealth and eHealth where various and optimization of data mining modeling for visualization
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data in medical health and electronic health, which have cial intelligence in COVID-19 drug repurposing,” The Lancet
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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
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Henan Province in 2020 (Grant number: 2021-ZZJH-233), [15] Y. Zheng, Y. Zhu, M. Ji et al., “A learning-based model to eval-
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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-
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