Review ArticleInfluential Usage of Big Data and ArtificialIntelligence in Healthcare
Yan Cheng Yang ,1,2 Saad Ul Islam,3 Asra Noor,3 Sadia Khan,3 Waseem Afsar,3
and Shah Nazir 3
1Foreign Language Department, Luoyang Institute of Science and Technology, Luoyang, Henan, China2Foreign Language Department/Language and Cognition Center, Hunan University, Changsha, Hunan, China3Department of Computer Science, University of Swabi, Swabi, Pakistan
Artificial intelligence (AI) is making computer systems capable of executing human brain tasks in many fields in all aspects ofdaily life. The enhancement in information and communications technology (ICT) has indisputably improved the quality ofpeople’s lives around the globe. Especially, ICT has led to a very needy and tremendous improvement in the health sectorwhich is commonly known as electronic health (eHealth) and medical health (mHealth). Deep machine learning and AIapproaches are commonly presented in many applications using big data, which consists of all relevant data about the medicalhealth and diseases which a model can access at the time of execution or diagnosis of diseases. For example, cardiovascularimaging has now accurate imaging combined with big data from the eHealth record and pathology to better characterize thedisease and personalized therapy. In clinical work and imaging, cancer care is getting improved by knowing the tumor biologyand helping in the implementation of precision medicine. The Markov model is used to extract new approaches for leveragingcancer. In this paper, we have reviewed existing research relevant to eHealth and mHealth where various models are discussedwhich uses big data for the diagnosis and healthcare system. This paper summarizes the recent promising applications of AIand big data in medical health and electronic health, which have potentially added value to diagnosis and patient care.
1. Introduction
AI is a field of computer science, which is capable of copyinghuman characteristics, capacity of learning, and storage ofknowledge. It executes human brain tasks in most of thefields in all aspects of our daily life using big data applica-tions. Super computers are analyzing big data using the algo-rithms of advanced deep learning machines, which hasallowed the improvement of output in the field. This fieldhas led very needy and tremendous enhancement in everyfield of life, especially in healthcare. Big data analysis isentered in biomedical sciences; the foremost source of bigdata has been discussed and explained, especially in oncol-ogy, cardiovascular disease, allergic diseases, clinical work,ear diseases, and so on. Along with this, it also touches someaspects of need to combine several pathological and clinical
sources and quality of life data [1] as well as the unprece-dented and quantitative datasets of neuroscience [2]. Medicalcare and treatment are getting enhanced day by day.Researchers are collecting data to make human care anddiagnosis disease efficiently and operationally. Medical doc-tors are skeptical about the help that artificial intelligencecan deliver to them in their medical practices. Studies haveexplored an understandable experience which includes allchallenges, risks, and comfort since the implementation ofAI programmed with the help of big data in healthcare withina contagious disease setting. The aim of the program was atdesigning a set of tools which can support an accurate, objec-tive, and clinical decision-making process efficiently [3]. Inbiomedicine, there are many ways to collect data fromobservation and experiments. Scientists are often strugglingto collect data about the disease and on how data are being
HindawiComputational and Mathematical Methods in MedicineVolume 2021, Article ID 5812499, 13 pageshttps://doi.org/10.1155/2021/5812499
generated for the treatment of disease. This biomedical infor-mation is to create thinking, managing, and analyzing andthe way they can transform into further scientific perceptionfor enhancing patient care. This is the major challenge for theNational Institutes of Healthcare (NIH) to lead the big datato knowledge representation. The agencies are making effortsto collect increasingly data with increasing research produc-tivity. Leading centers are developing to handle this type ofcases with large-scale data. They are going to research howmuch accurate equipment is required for the new generationof biomedical data scientists. In today’s world, NIH and BigData to Knowledge (BD2K) seek the position of data sciencesin biomedical research [4].
Health information technology is designed due to thesolution of complex problems such as health disparities.Mix results can be obtained by demonstrating a directimpact on health outcomes. For all these, we must knowabout collective intelligence, big data, informatics capacities,corresponding terms of smart health, knowledge exchangeknowledge, ecosystem, and situational awareness. Standards,guidelines, and objections are established by public healthinformatics for health equity and health disparities, whichwill increase health literacy and access to care [5]. Fodehand Zeng [6] have presented the notation of big data whichis used for three words: volume, variety, and velocity. This isthe foremost concept which is associated with the dimen-sion’s volume, and the variety is very relevant to the biomed-ical data gained by patients, including tabulated andstructured data. The biomedical research communities arestruggling to escalate in the clustering and analysis of bigdata. Mining big data probably holds plenty of informationand has a possible way and source for giving knowledge;data mining and machine learning methods are useful forsuch data series in a challenging task. Kavakiotis et al. [7]have discussed a lot of data on advance technologies whichare feasible for medical persons. Researchers have led a lotof research on diseases like diabetes mellitus, concerningthe different types of datasets that are mainly used. Thesehypotheses will lead us about the various knowledge of tech-nologies and diseases in the world. One should have suffi-cient data and knowledge about machines and technologybefore using it, as most of the machines used in medicaland healthcare emit radiation which is quite harmful anddangerous for humans and buildings. The proof of principlehas been provided for lung cancers which has been collectedfrom five locations. The euroCAT infrastructure has success-fully implemented as a radiation clinic. Radiation oncologyis facilitated as a euroCAT network and will facilitate themachine [8]. In daily clinical work, imaging data usage hasthe ability to improve cancer care by knowing tumor biologyand helping in the implementation of precision medicine.For accession of the tumor and its microenvironment, radio-mics helps in monitoring and evaluating the tumor featuressuch as temporal and spatial heterogeneity.
Limkin et al. [9] have provided a study case which assistsin ensuring the actual development and enhancement ofradiomics as a biomarker and facilitating its implementationin clinical practice. Gebremeskel et al. have proposed a datamining technique of optimization for envisioning of knowl-
edge extraction by examining clinical datasets to define thedecisive features using modeling techniques, capable of han-dling and analyzing large-scale data in its context [10]. In theanalysis, guidelines for using AI to speed up the drug’srecovery, so that AI’s vision is not strong but important,were used. The rapid development, power, and problem ofAI and network medicine technology have speed up thedevelopment of treatment in healthcare. The study is astrong proof for the use of AI-assisted tools for drugs andrepayment for human diseases, including COVID-19 intimes of fatal disease [11]. Various studies have been consid-ered for the qualitative analysis of the COVID-19 [12–14].These studies have mainly considered corona virus fromdifferent perspectives.
The exposure of corona virus disease 2019 puts anincreasing burden on the healthcare system. While infectedpatients have nonserious signs, which can be handled athome, some people develop serious symptoms and need tobe hospitalized. That is why it is important to effectivelyevaluate the severity of COVID-19 and identify the hospi-tal’s priorities. COVID-19 has been published in a numberof countries in the short term. By choosing a session as atime of patient discharge, including rescue techniques andstatistical analysis, it is time to seriously prepare it for deci-sion makers to be prepared for more hospital burden. A cubemodel was proposed to validate and verify the actual and con-firmed cases and deaths, which was then accepted by theWorld Health Organization. Often during the cholera disease,this research-assisted pairing can be learned. Four diagnosticmodels, including lymphocytes, lactate dehydrogenase,C-reactive proteins, and neutrophils, have been developedand validated using the XGBoost algorithm [15–17].
The contribution of the proposed research is to reviewthe existing research associated to eHealth and mHealthwhere various models are discussed. The approaches alongwith the uses of big data for diagnosis of disease in health-care system are elaborated. The study summarizes the recentpromising applications of AI and big data in medical healthand electronic health. The study will possibly add value tothe research in diagnosis and patient care.
This paper is structured as follows: Section 2 shows therelated work to the planned research. Details regarding theapplications of big data and AI are given briefly in Section3. Challenges and barriers faced by healthcare are shown inSection 4. The conclusion of the paper is given in Section 5.
2. Literature Review
Miller et al. [18] have presented an overview of biomedicaldata for the derivation of new knowledge on how clinicaldecision support tools can be developed from the wealth ofavailable data to modernize clinical care and scientificresearch of diseases related to neurodegeneration in the pre-cision medicine era. Garcia-Ceja et al. [19] have surveyedsensor data and machine learning about mental health mon-itoring systems. The main focus of the study is about mentaldisorders or conditions like stress, anxiety, depression, andbipolar disorder. It is suggested to guide the review of therelated categorization taxonomy study and present the
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overall phase of MHMS. Digital health technologies allowaccess to optimize clinical care delivery and clinical researchto access the information about age of healthcare. Despitethe usage of such technologies in research and clinical carefaces issues such as data quality, privacy, and regulatory,etc. Sharma et al. [20] summarizes think tank meetingproceedings with goals to delineate a framework for appro-priately using digital health technologies in research andhealthcare delivery. In Alzheimer’s disease (AD), clinicaldevelopment programs have failed to modify new diseasesand to establish quality work instrument for the treatmentof new and advanced diseases. The report is based on com-puter modeling and simulation, which plays a role of beingpowerful and observational in AD research [21]. Super com-puters are analyzing big data using algorithms of advanceddeep learning machines which have allowed the improve-ment of output like visual image interpretation to formerlyimperceptible levels and promised to change the practice ofmedicine. AI is a well-known field about what everyone isaware of; it is bringing a tremendous progress in such fieldslike automation of clinical decisions, disease detectors, imag-ing analysis in medical diagnosis classifiers, an interven-tional procedures, and it has the strength to significantlyaffect the practice interventional cardiology. This distinctivenature of interventional cardiology has made it a supremetarget for the evolution of artificial intelligence-basedapproaches which are delineated to enhance real-time clini-cal decision making, the elegant flow of work in laboratories,and standardized catheter-based procedures using advancerobotics. This study highlights the scope of artificial intelli-gence, its potential applications, and limitations in interven-tional cardiology [22].
The healthcare domain has obtained its effect by theinfluence of big data since the sources elaborated in the insti-tutions of healthcare are familiar for their dimensions, heter-ogenous complication, and excessive spirit. Big data’sanalytical techniques, tools, and platforms are perceivedamong numerous fields; their effect on healthcare institu-tions for fulfilling novel use cases for possible healthcareapplications shows promising research directions. In thiscircumstance, the success of these applications is completelydependent on the fundamental architecture and optimiza-tion of suitable and proper tools as signified by introducingresearch attempts. Palanisamy and Thirunavukarasu [23]have discussed many analytical areas that endure in thepatient care system from stockholders’ perspectives andappraise big data structure with respect to fundamental datasources, systematic capabilities, and application fields, whilethe intimation of big data that aids in the development ofhealthcare ecosystem is also discussed. The modern tech-nique, hidden Markov model (HMM), can be used to man-age massive data; the inference of the reproducible andinterpretable dynamic brain network can be made possiblein different dataset ranges including various other tasks[24]. The previous enhancements in ICT have indisputablyimproved the quality of people’s lives around the globe.Computer technology has led to very needy and tremendousenhancement in the health sector which is commonlyknown as eHealth (electronic health). For optimizing the full
and admirable benefits of this development, it is importantto implement it in a cloud-based environment. Besidenumerous and elegant benefits of eHealth in cloud comput-ing, its full optimization is still obstructed by facing somechallenges like privacy and security. The study mainlyfocuses on contemporary literatures of many techniques,and the mechanism is used to control privacy and security-related matters in eHealth. The robustness and fragility ofthese approaches were vocalized. 110 articles were reviewed,and several models were figured out in which solutions wereadopted in it. These models were compared, and the articleswere reviewed in which a standard and acceptable definitionof eHealth was given as it was recommended. Privacy andsecurity requirements were also discussed and provided asper the recommendation of the Health Insurance Portabilityand Accountability Act (HIPAA). Finally, a dependable andsecured architecture for eHealth was proposed that couldguarantee the effective, regulated, and dependable accessframework to health information [25]. It has also applica-tions in speech and language therapy. However, these thera-pies are often less willingly available for a few months afterstroke. Palmer et al. [26] have assessed a self-managed com-puterized speech and language therapy (CSLT) aiming todeliver more therapy to patients than they can accessthrough usual care alone. CSLT plus usual care significantlyimproved in individually relevant word findings, but theydid not improve in conversation. Schoettler et al. [27] havepresented a review of genome-wide association studies(GWASs) of asthma and those related to allergies, in whichhe concluded that childhood asthma is correlated to themost independent locus compared with other specifiedgroups of allergic and asthma cases, while adult asthma iscorrelated to fewer genes which can be considered as a sub-set of those similar with childhood asthma.
3. Applications of Big Data and AI
A lot of data in the world is available in different hospitalsand healthcare centers, which are collected from disease-affected humans. The amount of this data is increasing dayby day [28]. Researchers are trying to analyze the data andextract the diseases that affect the human body. Pobiruchinet al. [29] have extracted new approaches for leveraging can-cer in the development of Markov Models, from which theresearchers easily extract the clinical patient of breast cancer,which can be easily used for freshly affected bodies in thereal world. The study has mainly focused on publishingresearch on breast cancer, which is concerned with the clin-ical study of data. In this registry, normally, the patient iscovered for 8 years from the clinical registry report. Usingthe Markov model is very good for creating a good opportu-nity to make a future planning in the existence of data inhealthcare and clinical centers. This is useful to get an easyway to identify the patients and their affections in the future.A deep rule-based fuzzy system is used for accurate mortal-ity rate prediction in hospital intensive care units (ICU’S). Inpursuant to this approach, in each base building unit ofDRBFS, the same system of input has to be kept. In first 48hours in ICU, with heterogeneous featuring a set of
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admissions were extracted for mortality rate in hospital [30].With data concerned to the health records, recurrent neuralnetworks were used for disease risk prediction. The modelresults were better for small datasets, whereas for large-scale ones, the concerned model and its application to differ-ent patients across the population throughout hospitals werenot evaluated [31]. Epidemiologist is using electronic healthrecords which assist evidence provision for interval guide-lines and screening intervals while censoring interval andundiagnosed sickness make a path to issues like substantialanalytic risk estimation which cannot be addressed by usingKaplan-Meier methods [32]. Zhao et al. [33] have deter-mined the illustration of temporal data in electronic healthrecord (EHR); there were several methods based on sym-bolic sequence representation of time series data, whichcan be used in several ways, while several evidence relieson the standard of symbolic sequence representation bycomparing it to particular orders initiated by clinical expertswith the use of domain knowledge. In various otherapplications of big data based on trajectory data, the regularbehavior of private cars is extracted [34–36].
3.1. Disease Prediction and Big Data. Big data and artificialintelligence approaches are commonly presented in mHealthand eHealth applications for self-management and homecare of many long-term health conditions. Adaptation ofthese applications for self-management in asthma multifac-torial persistent diseases needs validation and evaluation inreal-life setups along with the enhancement of the patientlevel to personalize predictions concerning the control statusof asthma and increasing pitfalls. The study presents a short-term prophecy technique for controlling the asthma status,where multiple categorization models are considered trainsfor each observed framework along with obligatory prepro-cessing techniques to improve strength and effectiveness.The support vector machine, random forests, Bayesian net-work, and AdaBoost are considered machine learning algo-rithms used in this review [37]. Xin-Di et al. [38] havecomposed 252 formulae from the synopsis of prescriptionsof the Golden Chamber and a treatise on exogenous febriledisease, looking to identify the basic rules dominating thechoosing of herbal mixtures by probability models and bigdata technology. Pashazadeh and Navimipour [39] haveprovided a detailed and systematic review of the modernmechanisms related to healthcare applications in big datafields. Five of the discussed categories are heuristic based,hybrid mechanism, machine learning, cloud based, andagent based. Harous et al. [40] have enlightened the applica-tions of big data for obesity management using sensory andsocial data in mobile health architecture, which are mainlyproposed for assessing, detecting, and controlling obesity,which depend on a mobile phone to be used for complexoperations and processing of the data collected. Using thisnewly developed mobile architecture, one can tackle thechallenges of obesity control, monitoring, and prevention.Different algorithms have been used to generate featuredsuggestions, recommendations, and warnings to controlobesity and its related diseases. In the context of large data-sets of physical growth measurements which are regularly
used by health professionals, this paper is aimed at regener-ating a new growth chart for French children. 32 randomprimary care pediatricians and 10 volunteer general practi-tioners were selected across the French main regions wherethe electronic medical record software was used. All dataof physical growth was extracted for pediatric patients withanonymization. A new growth chart was derived whichwas then compared with the WHO chart and found itcorrect and error free [41].
Mechanical therapies are primarily prescribed for motor,duodenal, endocrine, urinary, respiratory, digestive, neural,and cardiovascular diseases. The speedy evolution of health-care is driven by the enhancement of technologies related tothe Olympics, biomedical, and scientific sciences. Appropri-ate medicine has been designed to implement specific andmore specific treatments for specific diseases and patients[42]. The healthcare industry was booming in the pharma-ceutical industry. Once again, like the confirmations, text,numbers, photos, and catalogs, any information that liststhe large and irregular discovery of various health servicemanagement systems lists patient information. At the sametime, hospitals were part of several geographical depart-ments in different locations. In different ways, fact providepatient health information from time to time. In responseto these problems, Damien is a one-sided solution to health-care interventions for a marine, vegan-based healthcaremanagement system [43]. The incidence of pediatric andpediatric diseases such as pneumonia and diarrhea are glob-ally challenging. The Patient Demographic and Health Sur-vey (DHS) deportation post from India once again createdcesarean section (CBNs) of the day and explored the loca-tion factors of childhood diarrhea. Location of freeware toolsfor DHS data, graphic structure learning with world score-based limitation, and hybrid structure learning algorithmsare described. The study identified the outcome of missingvalues, sample size, and constraints of knowledge based oneach of the structure learning algorithms and evaluates theiraccuracy with multiple scoring functions. [44]. Waring et al.[45] have helped healthcare specialists in the knowledge lit-erature review in the Greater East using automated machinelearning (AutoML) with the latest experience of machinelearning models “O-Shelf” with limited information dataspecialization. The possible opportunities and risk of usingAutoML in healthcare in the field are also located in theforeground, as well as the demands of AutoML in healthcarein response to the attack. The use of ML techniques has thepossibility to improve outcomes of health, reduce costs ofhealthcare, and demonstrate clinical research. The GreatestConventions Nowadays, using AI, previously discovered thatknowledge can be found in large collections. Clinical oncol-ogy data is a start to clinical practice and research with awealth of multithreaded documents and metadata. Most ofour clients include companies and employees. Inadequateinterventions, short-term stay, and vision can improvepatient care. Incorporating powerful AI algorithms has beeninstrumental in changing the presentation and commonsense of high-intensity radiation oncology. However, this ispossible by establishing a clinical scientific community withradiation oncology [46].
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3.2. Technological Innovations in Healthcare. Blockchaintechnology, such as the vascularization of inherited traits,router transparency, and identification information, wasintroduced in 2008 in the Bitcoin cryptocurrency. A widerange of domain-level domains engaged in the healthdomain. The purpose of this study is to systematicallyacquire startup publications and evaluate the Turkish posi-tion of the use/offer of blockchain to improve processesand services in healthcare, health sciences, and services san-itary [47]. There are some biological processes in which cellregulation and signaling are involved through the assemblyof protein complexes. When there is any change occurringthrough protein-protein interfaces, it will affect the forma-tion of multiprotein complexes. Due to this, sudden changeoccurs in the cell functions and the development of geneticdiseases occurs like cancer and develops drug resistance.These proteins are essential and lead to the formation ofmany resources for predicting their effects. It should preparefor how mutations affect the structure and body of multipro-tein complexes [48]. There are some models underdevelopment for the solution of two different problems com-putationally, to allocate the fund for infectious diseases inthe deserts and forests of Africa. Guidance about empiricaldata collection and theory development for the developmentin the future is provided [49]. Biomedical research is gettingcompletely changed with big data. The extraordinaryadvances of automatic data collection of large-scale clinicaland molecular data have caused prime challenges to datasimplification and analysis, summarized for the evolutionof new computational techniques. The development of thesystem with the efficient use of biomedical big data inparticular medicine will need noble scientific and technicalcreation in which engineering, infrastructure, financial man-agement, and projects will be included. In this study, howthe transformation of data-driven techniques provides theprobability to address many of these problems has beenreviewed, directing the elaboration of the theory on systemfunctions and the generation of technical models andassisting the design of clinical procedures in precision med-icine [50].
Ear disease and mastoid diseases can be easily handled ifearly detected with appropriate medical care. However,sometimes, we do not have specialists or we may have com-paratively low diagnostic precision if a new way of diagnosticpolicy is needed, in which deep machine learning can play anoble role. Cha et al. [51] have presented a machine learningmodel for automatically diagnosing ear disease with the helpof datasets having a total of 10544 images which weretrained using CNN to categorize internal eardrum and exter-nal auditory canal features into 6 categories of ear diseases,which has covered most of the ear diseases.. Inception-V3and ResNet101 are the learning models which have beenchosen for the time training and accuracy, which has anaverage accuracy of 93.67%. This classifier is trained in var-ious conditions and environments and gives better results ofdiagnosis, so it is more compatible for the use in a practicalenvironment. For various countries, EQ-5D-3L (three-levelEuroQol five-dimensional questionnaire) value sets areentrenched to evaluate the utility of the health state. Before
generating these values, analysts gather values for a precho-sen health state from a console who represents the publicand then use a mathematical algorithm to produce valuesfor 243 states. To estimate the rationality and correctnessof both the newer criteria and, earlier, in terms of forecastingof values for all health states and of the values of commonhealth states in specific, preexisting dataset has been usedthat accommodates VAS (visual analogue scale) values from126 students while each student has valued all 243 states. Asequence of representations was produced, and eventually,data were designed according to each model. Few of themwere used in the past [52]. Related to scientific activity, thisstudy shows a large data depository, containing epidemiol-ogy, clinical, and molecular data, which offers researchopportunities and is essential to assist scientific activity.The advantages of massive data may contain low-cost collec-tion. Restrictions may contain the cost and difficulty of datastorage and processing, which is essential for modernapproaches like formatting, analysis, and concerns aboutsecurity, accuracy, and reliability. It is explained that thesource of big data and tools for its analysis can be used tohelp and diagnose the treatment and management of derma-tologic diseases [53]. Diabetes is a condition in which glu-cose levels are lowered into the bloodstream with a slightdelay, causing the inability to metabolize it. When the insu-lin in the body does not respond properly or if the body doesnot produce it, this results in diabetes. Serious and long-termhealth issues are reported. The administration does notreally treat electives that include heart pain, lung disorders,skin and liver disorders, nerve damage, and noise damage;quick findings are rare. Thakkar et al. [54] has discussedsome valued techniques to excavate in the future and forthe diagnosis of diabetes.
Tandon et al. [55] present current issues and opportuni-ties to use blockchain technology to improve health perfor-mance. The SLR (systematic literature review) results showthat blockchain is being used to create new jobs to improvethe standards for the management, distribution, and pro-cessing of personal medical records. Implementing theblockchain technology is part of the healthcare system, atverity in the healthcare industry, where the significant valuehas been added by improving the performance, nearness,advancement of technology, protection, security, and pri-vacy of information management systems. The collectionand elaboration of information on public technology hasbeen incorporated by the government and by the real estatecompanies of the whole world as a strategic solution toalleviate the information of COVID-19 that prevents infor-mation. However, the law regarding the end-of-life monitor-ing system to monitor those who are unable to read thefollowing documents for the help in using the documentsof power of the public in the world. EHRs contain valuableinformation on the progression of Rangatahi’s disease andthe tragedy of treatment. In the heart failure study, Estherand her colleagues proposed a scheme for extracting short-term sequential samples from CCE data that gradingimproves phenotype and prediction and is also professional.Brief information on EHR on disease progression and treat-ment outcomes are given. This article presents a method for
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representing transient transitional CCE observations up tomachine learning. Using clinical data from patients withheart disease, EHR drug transfer and provisional representa-tion from diagnostic and class records for prescribing activ-ities are given [56]. Virtualization (AI) and machine learningare having a real impact in many areas of the healthcareindustry. The cancer site is a global phenomenon. Effortsto reduce the chances of accidents should be timely. How-ever, metastatic development, serotonin frequency, and druginhibition are observed. It is important to find new bio-markers that stimulate group resistance and improve drugdelivery to improve carriers to improve carriers [57]. Time-line risk assessment tools for heart failure use a databasedesigned with realistic, one-time clinical document scansand low accuracy. The purpose of this study was to createa comprehensive 30-day plan for unannounced announce-ments and accurate forecasts for all. All-cause mortality(ACM) connects clinical and clinical information availableon the electronic medical record system. There are three pre-dictive indicators for a 30-day unstructured reading or ACMcreated using a step-by-step method: (a) index admissionmodel, (b) feature-aggregated model, and (c) index dis-charge model [58]. The study has talked about artificialintelligence that it has the ability to mimic human character-istics, knowledge storage, learning capacity, and so on. Thereare a majority of techniques which have been already appliedin medicine and healthcare, such as the C-statistic model,which was used in North Carolina hospitals as the finestmodel for the prediction and enhancement of the efficiencyof preventive medicine [59]. Table 1 represents some of theexisting research work available in the literature.
3.3. Cardiovascular. Cardiovascular imaging is getting chan-ged with the rise of data science. There are many techniqueswhich have been applied in cardiovascular medicine forexploring different types of diseases, as well as to improvepatient care quality and mortality rate. The potential of AIin cardiovascular medicine is tremendous, although igno-rance of the challenges may overshadow its potential clinicalimpact [60]. There are some complexities like efficiency,timing, and missing diagnoses which occur in all stages ofthe imaging chain. The dependency of AI’s applications fallson vigorous data, suitable computational techniques, andtools. While in some areas, it depends on the validation ofits clinical application to image sectionalization, automatedmeasurements, and diagnosis. Cardiovascular disease hasnow accurate imaging combined with big data from theeHealth record and pathology to better characterize the dis-ease and personalized therapy [61]. Research has exploredthe area and presented studies on the available literature inthe area [62, 63].
3.4. Big Data and Disease Identification. In daily clinicalwork, imaging data usage has the ability to improve cancercare by knowing tumor biology and helping in the imple-mentation of precision medicine. For accession of tumorand its microenvironment, radiomics helps in monitoringand evaluating tumor features such as temporal and spatialheterogeneity [64]. Asthma is inconsistent in clinical associ-
ations and poor replication of genetic associations. Asthmais not a single disease but a group of similar diseases withsome clinical manifestations. Big data for the disease is notproviding a solution, just giving information about the dis-ease. Bayesian and frequent approaches make us understandabout the ethnology of diseases commonly used in statisticalmachine learning and can be applied to both big and smalldata in healthcare research. We need to know about the real-ity of asthma and not to just give predictions only. We haveto get awareness from methodological polemics on data sci-ence. The Bayesian and frequent paradigms of the presentartificial dichotomies are given. Big data are required todivide the asthma into subtypes to get the solution. Exploit-ing disaggregated phenotypes and genomic research and theexploration of lung function as an intermediate phenotype.Cohering inductive and deductive statistical approaches toepidemiology pragmatic bayes and unifying graphs are pre-sented [65]. There are techniques and methods by whichpatients can be classified effectively as those who haveasthma and those who do not have those in long-termremission [66]. VA-PODR (Veterinary Affairs AccurateOncology Data Collection) is a worldwide large repositoryof well-known data concerned with diagnosed patients ofcancer in VA’s (Veterans Affairs) department. This dataconsists of long-term clinical data from the nationwide Vet-erans Affairs eHealth record, medical photography data,tumor sequencing targets, pathology slides, and the VeteransAffairs central cancer registry as well as CT (computertomography) scans. A subset of this data storage is availablein GDC (genomic data commune) and the TCIA (TheCancer Imaging Archive) [67].
The allergic conjunctivitis population has a frequency of15–20%. However, due to the lack of understanding, themulticause of chronic obstructive pulmonary disease is thatthe environment, residence, and direct distribution havecomplex relationships. The benefits of medicine for chronickidney disease patients and the type of treatment for demen-tia, as well as the possibilities for other diseases that need tobe known by patients that will help patients according to thesymptoms and risk factors of the intervention’s socialempowerment and rehabilitation of people with chronicobstructive pulmonary disease to reduce the body helpingto create permanent solutions and prevent the escalation ofsocial ills [68].
In biomedical repositories, the size of somatic genomesis getting increase. So predicting documents related to can-cer sets using mathematical algorithms are described. Min-ing models of conventional gene-based somatic cancer areunhampered by somatic gene ranking and feature extractiondue to high computational cost and memory of large data-sets. A wide range of characteristics, preferences, and featuresqueezing procedures is available, and they are normallydeployed in many areas. Each of these procedures tries toextract and squeeze some new irrelevant characteristic fea-tures from the trained datasets, having a goal of increasinglyaccurate results for the newly arranged documented data.Extraction or squeezing of data is an activity of deliveringrequired and related data from a large collection of datasetsaccording to the given information. There are criteria for
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ranking the information which is sorted as the best resultswill have the top priority so it will be on the top of the pro-vided list. Experimental results have different cluster sizeswith different gene features for clustering of somatic docu-ments. And their generated results prove that the existingmodel has a high quality of computational clustering [69].
4. Challenges and Barriers Faced by Healthcare
The healthcare organizations are facing many challenges likeanalyzing large-scale data. With the rapid increase in health-care applications, many devices are used to generate veritiesof data and many of them are used for better analyzing dataand for better decision making [70]. The following are thechallenges which todays healthcare organizations are facing.
4.1. Social Media. Analysts of today’s era are giving name tothe period that we are living in “an era of fake news” basedon the spread of misinformation produced, which may bespread intentionally or unintentionally. As it is affecting allareas and fields of life, it also creates problems and issues
in fields related to health, where it may cause to detain orstop effective care, while in some cases, it is menacing thelives of individuals. Wang et al. [71] have exposed the pres-ent proof and made it clear in an understandable way; themechanism of misinformation spreads. The most substantialand considerable topics include misinformation related tovaccination, Zika virus, and Ebola, even though others, suchas fluoridation of water, nutrition, cancer, and smoking arealso highlighted. Most studies employed content analysis,social network analysis, or experiments, drawing on dispa-rate disciplinary paradigms. Respective systems are used toclassify this data based on the result and condition of eachsentence [72]. Future research should examine the suscepti-bility of different sociodemographic groups for misinforma-tion and to understand the role of belief systems on theintention to spread misinformation.
4.2. Knowledge about Machines and Technology. Most of thetechnicians or users of the machines do not have enoughknowledge on using the technologies/machines [73]. Toovercome this problem, technicians and operators should
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
13Roadmap for innovation-ACC health policy statement on healthcare transformation in the era of digital health,
big data, and precision health2018
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
23Electronic health data and machine learning for the prediction of 30-day unplanned readmission or all-cause
mortality in heart failure2020
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
7Computational and Mathematical Methods in Medicine
Article Number DisciplinePaper
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.
8 Computational and Mathematical Methods in Medicine
have sufficient data and knowledge about machines andtechnology before using it, as most of the machines used inmedical and healthcare emit radiation which is quite harm-
ful and dangerous for humans and buildings. The euroCATinfrastructure needs to be implemented as a radiationclinic [8].
Article
Number
PublisherPaper
Location of conference Total Publication topic R.Pap
Figure 3: Details of the IEEE library from various perspectives.
Publication title Pulications Subject area PaperAccess R.P
Figure 4: Details of the search process from various perspectives in the ScienceDirect library.
9Computational and Mathematical Methods in Medicine
4.3. High-Power Consuming Technologies. As these artificialmachines can perform a complex tasks; many of the technol-ogies operate on large scale in large areas for a long time,which consumes much electric power while there is a bigissue of power in most of the developing countries whichneeds to be solved and adjusted to give plenty of facilitiesin healthcare.
4.4. Expensive. This is the generation of robotics and artifi-cial intelligence which has large significance in the field ofhealthcare. However, technologies, machines, and data stor-ages, either cloud database or our own database, are mostlynot affordable by the organizations because of high prices.By using single-cohort model parameters, we can obtusethe price issues which we are facing and we will face in thefuture [74]; this will add a significant value to the medicalorganizations as well as patients in rural areas who cannotafford and do not have access to these technologies.
4.5. Transformation. A healthcare transformation is amethod of sharing thoughts of a company to work on that.It has the importance as the old technology is transformingto a newly developed and evolving technology based oninnovation, which may include digital health, eHealth,mHealth, and sensor-based technologies. Such enhancement
in health may be significant in identifying diseases on thenext level. There are some things which need to be observedbefore transforming shifting from traditional healthcare to vir-tual and real-time diagnoses which remains lacking in trueevaluation whether these inventions are really improving andovercoming the healthcare quality or not. If any of the newtechnology is adopted which is not based on evidenced caremay risk unintended outcomes like privacy breach, increasein care cost, etc [75]. Distributed ledger technology needs tobe implemented to ensure the integrity and authenticity ofdata produced by embedded systems [76].
The existing literature was studied to know about thedetails of research available in the area of healthcare basedon AI and big data. Figure 1 represents the details of the lit-erature in the Springer library from various perspectives asgiven in the figure.
Figure 2 elaborates the details of the search process inthe library of ACM.
The library of IEEE was also searched for getting theassociated details. Figure 3 describes the details of the searchprocess for the IEEE library.
Lastly, the ScienceDirect library was tried and the details ofthe library from various perspectives are shown in Figure 4.
This library was further elaborated and some of thedetails are shown in Figure 5.
Figure 5: Details of the ScienceDirect library.
10 Computational and Mathematical Methods in Medicine
5. Conclusion
Big data and AI have revolutionized everything that hasbrought comfort to people’s life in all aspects of daily life.Especially, it is bringing innovative technologies in health-care systems. It has transformed the conventional and tradi-tional health system to a technological systems in the formof cardiovascular, oncology, ear, asthmatic, allergic, cancer,and or any other diseases. Big data has made it easy todiagnose disease through virtual and real-time systems.The contribution of this paper is to review the presentresearch applicable to mHealth and eHealth where variousapproaches and models are discussed which use big datafor diagnosis and healthcare system. This paper has summa-rized the current encouraging applications of AI and bigdata in medical health and electronic health, which havepotentially added value to the diagnosis of disease andpatient care. The proposed research will help researchers todevise new solutions in the area of healthcare.
Conflicts of Interest
The authors declare no conflict of interest.
Acknowledgments
This research was financially supported by the Humanitiesand Social Science Fund of the Education Department ofHenan Province in 2020 (Grant number: 2021-ZZJH-233),Luoyang Humanities and Social Science Fund in 2021(YB-153), provincial and ministerial preresearch projects inLuoyang Institute of Technology and Science of 2019, andthe general grant project of the Provincial Social ScienceAchievements Review Committee in 2021 (Grant number:XSP21YBZ077).
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