Precision Medicine Informatics: Principles, Prospects, and Challenges

Muhammad Afzal1, Member, IEEE, S.M. Riazul Islam2, Member, IEEE, Maqbool Hussain1, and Sungyoung Lee3 1Department of Software, Sejong University, Seoul, South Korea 2Department of Computer Science and Engineering, Sejong University, Seoul, South Korea 3Department of Computer Science and Engineering, Kyung Hee University, Yongin, South Korea Corresponding author: Muhammad Afzal (e-mail: [email protected]). This research was supported by the Ministry of Science and ICT (MSIT), Korea, under the Information Technology Research Center (ITRC) support program (IITP-2017-0-01629) supervised by the Institute of Information & Communications Technology Planning & Evaluation (IITP). This work was supported by an IITP grant funded by the Korean government (MSIT) (no.2017-0-00655).

Abstract Precision Medicine (PM) is an emerging approach that appears with the impression of changing the existing paradigm of medical practice. Recent advances in technological innovations and genetics, and the growing availability of health data have set a new pace of the research and imposes a set of new requirements on different stakeholders. To date, some studies are available that discuss about different aspects of PM. Nevertheless, a holistic representation of those aspects deemed to confer the technological perspective, in relation to applications and challenges, is mostly ignored. In this context, this paper surveys advances in PM from informatics viewpoint and reviews the enabling tools and techniques in a categorized manner. In addition, the study discusses how other technological paradigms including big data, artificial intelligence, and internet of things can be exploited to advance the potentials of PM. Furthermore, the paper provides some guidelines for future research for seamless implementation and wide-scale deployment of PM based on identified open issues and associated challenges. To this end, the paper proposes an integrated holistic framework for PM motivating informatics researchers to design their relevant research works in an appropriate context.

INDEX TERMS: Precision Medicine; Bioinformatics; Informatics; Artificial Intelligence; Internet of Things; Big Data; Clinical Decision Support; Deep Learning; Machine Learning.

I. INTRODUCTION Precision Medicine (PM) is one of the fledging paradigms A. BRIEF OVERVIEW OF PRECISION MEDICINE that the next generation healthcare solutions sprouting The concept of PM emerged as a healthcare-aligned towards. It helps us grow more knowledge on human mainstream discipline through its formal launching in physiology by means of genomic insights and advances in 2015 as the prevention and treatment that consider the technology. PM is an attention-grabbing area of research individual variability [1]. To put it simply, PM refers to for medicinal community with various multidimensional serve the right patient with the right drug at the right time, prospects. At the same time, it is quite exciting for by considering the molecular events that are accountable informatics community with enormous potential to for the disease [2]. The term precision medicine is often research and exploit the technological perspective for the muddled with personalized medicine [3], [4] due to the common goals. It is however challenging for either inclusion of the word “Individual” in the definition of the community to absorb the technicalities involved in PM itself. However, the PM provides a more drawing relationships among different prospects in this comprehensive and precise meaning to what cross-disciplinary research field. From informatics individualized and personalized medicine were representing over the years. Unlike personalized viewpoint, PM introduces a new level of challenges on the medicine, the notion of PM is to combine clinical data developing informatics solutions including omic with population-based molecular profiling, informatics and health informatics for a more focused and epidemiological data and other data so as to make clinical precise patient care. decisions for the benefit of individual patients [5]. The personalized medicine terms is used dominantly in some regions of the world and in a commentary, the authors integrated expertise on the different but interrelated termed PM as a part of personalized medicine [6]. The domains including, to the minimum, physicians, other terms they mentioned include “individualized biologists, and computer scientists. It is clear that two medicine,” “genomic medicine,” “stratified medicine,” aspects of participation in PM are taken of utter “pharmacogenomics,” and “P4 medicine”. This study, importance: (i) the healthcare system in order to deliver however, uses the term “precision medicine” as a main precise diagnosis and therapies and (ii) the scientists to subject in the search queries and focuses on the same in develop the infrastructure, principles, and insights into PM the contents to avoid any confusion with other competitive [9]. terminologies. The paradigm shift to PM from the traditional medicine B. STUDY OBJECTIVE AND CONTRIBUTIONS approaches can be thought of as a movement from In this study, we explore an informatics perspective of PM generalization to personalization. In other words, unlike describing principles, issues, challenges and prospective the current approaches that consider a general solutions. Moreover, we include different initiatives understanding based on the average conditions and around the world on the subject and a historical journey to

FIGURE 1. Traditional and PM approaches with key differences on classification factors and treatment outcomes. clinical outcomes for the patients of interest, the PM create a case for bridging the current evidence-based approach works based on the individual variability in medicine (EBM) with PM. The existing studies [8][10] genes, environment, and lifestyle [4]. Consequently, provide a big picture of an informatic research and whereas current approaches might be successful for one envision the need of advanced tools and technologies to group of patients and not for the other, PM-based support PM. Also, we can find a fair set of literature approaches are more likely to be effective for each group [11][12] that discuss about the PM realization and of patients. The abstract level comparison of PM with implementation issues and challenges. The larger set of current approaches is depicted in Figure 1. The schematic existing studies is available on the molecular and -omic shows the key differences between traditional and PM information in terms of efficient algorithms and methods approaches in terms of classification of patient population for genome mapping, alignments, variant callings, and – whereas PM classifies the patients based on risk and annotations. Similarly, the clinical aspect has been identifies the surveillance for preclinical disease, researched and implemented in the long run without conventional approaches look for the signs or symptoms aligning the focus to consider the other aspects of PM - and deal the patients equally if they share the same genome and environmental data. Moreover, PM is symptoms [7]. Because of this generalization, in recognized as tantamount to a technology-driven approach conventional approaches the benefits are not reached out to all the patients; however, in PM, each group of patients get equal level of benefits as they are treated rightfully with the right treatment. The PM approach attracts multiple stakeholders in the biomedical enterprise, including care providers, payers, researchers, and patients [8]. Also, it seeks for the FIGURE 2. PRIMA flow diagram for literature survey of the articles included in the study. [13], therefore, it embroils algorithm and technology in its PM and presented a conceptual model that meaning. integrates both the paradigms. This study provides an overview of existing efforts on PM • The study analyzes the implementation informatics agenda, tools and techniques in three areas of challenges of PM and highlights the design informatics – bioinformatic, clinical informatic, and issues of clinical decision support systems. It participatory health informatic, security, standardization, takes into account the integration and integration, and implantation challenges, and the design of standardization challenges in terms of data holistic PM framework to enlighten the futuristic privacy, safety, security, and exchange endeavors in the area of informatic research and standards for interoperability, and issues of implementation. In this regard, the contributions of this realization and design of an ecosystem for PM. paper are outlined below: • Based on identified limitations on PM • To encourage the principle of ‘learn to exist’ implementations, we propose a holistic rather than to compete, this study compiles the integrated PM framework that assists computer state-of-the-art views on PM to achieve a scientists, health- and bio-informaticists to carry pragmatic balance among the existing forward the challenge of successful realization approaches. The study adds on the of PM. reconciliation strategies between the existing evidence-based medicine (EBM) and emerging C. LITERATURE SURVEY METHODOLOGY PM approaches. Objectively, we employed the PRISMA (Preferred • To cover an inclusive picture of PM from tools Reporting Items for Systematic Reviews and Meta and technologies perspective, we elaborate and Analyses) [14] method for literature survey based on the generate a comprehensive summary of process followed in [15] with additional customizations in prominent programs, tools, frameworks, and the inclusion/exclusion criteria. We ran search queries on platforms in three aspects of informatics: two search engines viz. Web of Science and PubMed and bioinformatics, clinical informatics and linked all the search results into a local repository. A bulk participatory informatics. of peer-reviewed articles are checked for duplication and • The lifelines of PM- Big data and artificial the abstracts are screened to exclude all those articles that intelligence (AI) are included and elaborated in are focused either on biology, molecular and/or clinical the study to draw a useful relationship model perspective or unavailability of the full-text documents. with PM. The rest of the articles are checked for eligibility criteria • The internet of things (IoT)-enabled healthcare to include articles focusing on the topics noted earlier with has potentials to be a part of PM. In this context, PM as a primary content. It should be noted that some of we briefly discussed advantages of IoT-aided the articles are cited just for general reference on the topic even though the central content therein is not PM. For from the perspective of EBM proponents. As we learned example, [16] that talks on IoT in healthcare is a topic- in the preceding section, PM focuses mainly on the oriented citation rather than a PM-focused citation. individualistic behavior, a deviating scenario from the Similarly, we also referred to few popular websites and EBM. As shown in Figure 3, there also exist differences blogs, particularly, where the contents were of between EBM and PM in terms of the basic elements in introductory nature such as PM global initiatives. The decision-making process. However, both share similar Figure 2 explains the steps taken in the entire literature characteristics on multiple grounds. In an editorial, survey process. The number of articles excluded at authors opinioned that EBM and PM can be more different stages and the final set of articles included in the advantageous if they can adopt the principle of ‘learn to study are explicitly mentioned. exist’ in a symbiotic relationship to attain a pragmatic The rest of the paper is structured as follows. Section 2 balance between them [18]. describes the need of bridging the gap between EBM and PM. Section 3 explains the enabling tools and techniques They further hinted to an important factor of bridging the of PM. Section 4 is dedicated to discuss the Big Data and two paradigms – if we fail to do so it might turn out with AI in PM followed by section 5 that highlights the role of non-integrable outputs to address the health requirements, IoT in PM. Section 6 analyzes the implementation they originally set out to address. Similarly, authors challenges while section 7 focuses on the global initiatives concluded in their study [19] that EBM and PM regarding PM. In section 8, we provide the future direction complement rather than oppose one another although and presented our proposed integrated framework for PM. these approaches have their own merits and shortcomings. The final section concludes this systematic survey. However, the efforts to reconciling EBM and PM demand a clear understanding of the fundamental differences II. BRIDGING EBM AND PM between them. We investigate the differences and EBM has long been utilized in healthcare environment to similarities between EBM and PM and present the serve different purposes like supporting clinical decisions, findings in Table I. medical education, and health awareness. According to the The co-existence of EBM with PM amid the differences comprehended definition described in [17], EBM is the mentioned in Table 1 raises several challenges in terms of use of evidence collected from well-made research volume, format, and structure of data. We turn out few of formulated in primary studies such as meta-analyses, the challenges that are certainly required to be sorted out systematic reviews, and randomized controlled trials used making the amalgamation of EBM and PM a success. In for improved decision-making in medicine. In this way, Table II, some of the challenges are presented with EBM approximating the “one size fits all” implies the tentative solutions with the aim of bridging the two scenario of applying to all although it may not be exact paradigms.

FIGURE 3. The decision-making basic elements of EBM in (a) and PM in (b); which indicates that all the elements in (a) are included in (b). TABLE I SIMILARITIES AND DIFFERENCES BETWEEN EBM AND PM

Similarities • Both EBM and PM have the objective of providing better decision on patient health problem. • Both demand high quality and reliable evidence for the care of patients. However, the meaning of evidence could be different in two paradigms. • Respecting patient in terms of either preferences or lifestyle is a part of both EBM and PM decision-making basic elements. Differences • EBM projects “one size fits all” approach and does not provide adequate solution for outliers. By contrast, PM deals with the outliers and projects the idea of “one size doesn’t fit all” scenario [20]. • EBM is cognitive-biased on occasions where clinicians set the goal and question for the trials and may favor the publication based on reputation, the product of manufacturer who funds the study to be conducted [21], [22]. PM, on the other hand, relies on patient information that are existed rather than to rely on hypothesis only. • Since EBM relies on RCTs, outcome of RCTs are received in the form of either benefit, no effect, or adverse. In case of PM, the outcomes shall always be beneficial because they are target oriented that may leads to invent a new drug for the treatment [23]. • EBM over-emphasizes the clinical consultation and is mainly concerned about the people who seek care. It underestimates the power of social networks where people can inform each other about their health problems [24]. Since it focuses on individual preferences, PM thus encourages the emerging ways of data curation from diverse sources.

TABLE II CHALLENGES AND POTENTIAL SOLUTIONS OF RECONCILING EBM AND PM

Challenges Prospect solution • Analysis of voluminous data resided in different • The proponent experts from both EBM and PM databases paradigms need to form a consortium/body to • Bringing together data of various formats such as construct a unified architecture on the common clinical and molecular. grounds to revise the basic elements of clinical • Lack of standardization of data entry and storage decision making. • Understanding the paradigm shift from therapy to • Revisions and update of the guidelines of prevention, thus ultimately leading to clinician-to- developed for EBM, for instance, the criteria of patient communication and citizen-centered RCTs structure, conducting, and evaluations. healthcare [25]. • Devising a method to include patient input in the • Current published research has minimal patient input future research publishing. [24], thus it requires to include larger patient input in the future publishing.

III. PM Enabling Tools and Techniques sharing. In order to describe the diverse set of PM enabling Precision Medicine introduces a new level of challenges for tools and techniques, we classify the tools in Figure 4 based developing informatics solutions including –omic on three areas of informatics: bioinformatics, clinical informatics and health informatics for a more focused and informatics, and participatory health informatics. precise patient care. The informatics solutions range from data curation to processing, interpretation, integration, A. Bioinformatics Tools presentation, and visualization. The need for such enabling Bioinformatics refers to the establishment of an informatics solutions have been realized and discussed in infrastructure to provide means for storing, analyzing, array of studies [2], [26]–[30] with a central point of integrating, and visualizing large amounts of biological data requirement of tools and techniques for voluminous, and related information and providing access to it using complex, and heterogeneous data processing, integration, advanced computing, mathematics, and different and interpretation as well as knowledge acquisition and technological platforms [31]. The term ‘big data’ resonates FIGURE 4. Classification of enabling tools and techniques in three areas of informatics: clinical informatics, biomedical informatics, and participatory health informatics in the contents while talking about bioinformatics because and other computational tools [49]. On top of that, there big data technologies are required to process and analyze exist a few other language-specific tools, libraries, and large genomic data sets [32]–[34]. Several tools are software packages in bioinformatics such as BioJava [50], designed to work on genomic data at different levels. For BioPHP, BioPerl [51], PioRuby [52], and BioPython [53]. instance, GMAP (genomic mapping and alignment program) [35] is a tool for next generation sequencing with B. Clinical Informatics Tools a key functionality of sequence mapping and is designed to Clinical informatics is a subfield of health informatics, work on genomic, transcriptomic, and epigenomic data. which focuses on the usage of information in support of Other similar tools used for –omic data processing, patient care [54]. Over the last two decades, clinical searching, and alignments include BWA (Burrows-Wheeler informatics has progressed with an array of tools and aligner) [36], STAR (spliced transcripts alignment to a techniques including computerized entry systems, reference) [37], GATK (genome analysis toolkit) [38], analytical tools, decision support tools, and other clinical BLAST [39], DIAMOND [40] and others. The reporting techniques appeared to assist healthcare OmniBiomarker is a web-based application developed for professionals in different aspects. One of the related areas biomarker identification that utilizes a curated knowledge in clinical informatics is clinical information extraction, on base of cancer-genomic for analysis of high-throughput which a significant volume of research had been conducted data [41]. Similarly, a number of tools are available for – over the years. A methodological review [55] reported a omic data modeling such as, CODENSE (coherent dense wide range of information extraction frameworks, tools and subgraphs) [42], MEMo (mutual exclusivity modules in toolkits including cTAKES [56], MetaMap [57], MedEx cancer) [43] and WGCNA (weighted correlation network [58] and others. Another related aspect of clinical analysis) [44]. The leading tools available for the molecular informatics is healthcare data analytics. The analytics area and biological data analysis among others are Geneious itself is huge and we do not necessarily aim to cover every Prime [45], Cytoscape [46], [47], and Gephi [48]. There is aspect of it. However, the popular tools and techniques that a cloud-based integrative bioinformatics platform for focused on clinical aspects of the medical data are precision medicine called G-Doc Plus for handling discussed. This is to emphasize that healthcare data analysis biomedical big data using its cloud computing resources area is not limited to clinical data analysis rather it encompasses the combined analysis of phenotypes and genotypes. Data science tools in general have been used at IV. Big Data and Artificial Intelligence the same pace and importance as of clinical data analysis. In medicine, the application of AI has two divisions: virtual For instance, RapidMiner [59] and KNIME Analytics and physical. Whereas the former is characterized by Platform [60] are the leading data science tools that are machine learning (ML) and/or deep learning (DL), the latter equally applied for clinical data analysis. comprises physical objects, medical devices, and sophisticated robots [69]. In fact, the use of big data and AI C. Participatory Health Informatics Tools is a central aspect of PM initiatives [70] and some even PM expands the scope of medical care as most of the phrased it “there is no PM without AI” because of its population spends more time outside than the time in the fundamental requirement of computing power, algorithms physician’s office. It demands a deeper consumer machine learning and deep learning), and intelligent participation to collect information about a person’s approaches that uses the cognitive capabilities of physicians lifestyle and environment e.g. physical activity, dietary on a new scale [71]. Deep learning has been widely used for information, sleeping patterns, and other environmental clinical information extraction, phenotype discovery, image conditions [8]. A significant number of systems and analysis, and next generation sequencing [72], [73]. AI applications is added to the portfolio of quantified-self upsurges learning abilities and offers decision-making programs and digital health in recent years due to the capabilities at a scale to transform the healthcare future [74]. increasing trends in wearables and mobile technologies Therefore, physicians in everyday practice get pressure to [61]. Mining Minds (MM) project, is aimed at developing a look around innovations spreading over faster than ever novel framework for mining individual’s daily life data through the use of disruptive technologies and exponential produced from diverse resources [62]. The objective of the growth of healthcare data- the “Big data” [75]. Big data has MM project is in line with other endeavors such as Google gained a growing attention from data-oriented enterprises in Fit [63], Samsung Health [64], Fitbit [65], and Noom [66]. private and governmental sectors [76]. Despite the fact that However, it is more comprehensive and novel in terms of we are living the in the age of big data, however, the big knowledge acquisition and context-aware personalized data by itself is of no use without the processing using AI knowledge-based service support. Currently, these tools techniques which make it useful thus brings the potential to and services are geared towards nursing user health status transform the current clinical practice [77]. AI techniques, for physical activities, diet, and somewhat sleep patterns such as applications of machine learning on big data, are and as well as the environmental factors. In the era of PM, changing the way physicians make clinical decisions and it is required to channelize such efforts in a way to supply diagnosis. Big data analytics using PM platforms has consumers' health monitoring and environmental therefore the potential to include data of millions of patients information to their respective health providers for assisting for exploration and validation [77]. It is of more importance in decision making. The genomic information could better to understand interrelationships among big data, AI (ML be interpreted with this information and it will thus assist and DL), and the PM. Developing a PM platform or relevant the physicians to precisely diagnose a disease and treat. tools and services requires access to big data and processing There is a lot of other platforms and frameworks which (big data needs AI approaches including ML and DL cannot be put in a specific category rather then they are variants. Figure 5 illustrates the relationship of big data and enablers for data analysis such as platforms for big data AI in PM derived from the illustrations presented in [70], analytics including Apache Hadoop (MapReduce) [67] and [77]. IBM Infosphere Platform [68]. A selected list of tools for – omic data processing and biomarker identification is Many technology companies including IBM with a flagship platform of IBM Watson, Google with DeepMind, and provided in a study on -Omic and EHR Big Data Analytics others such as Apple and Amazon, are investing heavily in for Precision Medicine [29]. health care analytics to facilitate PM [70], [71], [77]. Despite the facilitation and improvements powered by AI D. Summary of PM Enabling Tools and Techniques for genomic and other omic data processing and analyzing, The prominent tools and platforms to support PM in data there still exist various challenges. In a review [78], authors processing, analysis, interpretations, sharing, and focus on AI applications of next generation sequencing and visualization reported in various studies are available under cancer genomics testing required for PM. In Table III, we public and commercial licenses. It is important to note that gathered a set of key benefits of AI in the era of PM and the some of the available platforms and tools are domain associated challenges. independent and are used for data analysis of any domain. For instance, the RapidMiner Studio [59] is a cross-platform data science tool, clinical informaticians use it for clinical data analysis and very recently, bioinformatics tools start integrating it in their workflows for enhanced data mining, analysis, and visualization.

FIGURE 5. Illustration of relationship between big data, artificial intelligence, and precision medicine

TABLE III KEY TOPICS WITH BENEFITS ON THE LEFT (GREEN ICONS) AND CHALLENGES ON THE RIGHT (RED ICONS)

Key benefits of AI in PM Challenges

Ground truth scarcity for validation of benefit Variant calling Literature Mining

AI algorithms leveraged to AI algorithms are utilized enable variant calling from for entity and relation Obtaining statistically significant patient NGS data. extraction from published outcome data is challenging. literature.

Transparency and reproducibility Companies, platforms, and publications offer

limited information for public consumption Variant interpretation and reporting .

AI algorithms are used to facilitate the process of variant classification Patient / physician education and to ease manual curation. Both patient and physicians should get precision medicine related education to enjoy the outcomes brought by AI and big data.

V. Role of IoT in Precision Medicine As noted in the early part of this paper, PM primarily The internet of things (IoT) enables us to introduce involves three categories of data – clinical data, genome automation in nearly every field and healthcare is one of the data, and environmental data. On the other hand, a simple most important and attractive application areas of this and brief description of how an IoT-based healthcare auspicious technology. The IoT uprising is reshaping system works can be presented as follows. First, the IoT modern healthcare with propitious technological, medical sensors and devices directly connected to the economic, and social prospects. The role PM plays can be patient's body of interest. Sensors collect various further enhanced by integrating the IoT. physiological conditions and vitals. Accumulated data are preprocessed and organized and are subsequently analyzed. supply chain control and monitoring is important for all The data are stored in the associated medical service industries, it is more vital for healthcare industry in provider's cloud storages for aggregation. Depending upon general and PM in particular. For example, it will not be the analytics and aggregation results, patients can be possible to have a quick substitute when a shipment of monitored from distant places and necessary actions are medicine that is personalized for the DNA of a patient with taken following predefined standard rules and guidelines. a life-threatening illness is spoiled or stolen. Interested readers are referred to the compressive study reported in [123] to grow more knowledge on the IoT-based C. Medical Error Minimization healthcare. Clearly, the IoT can prominently assist PM by The main objective of PM is to delivery of optimized arranging the environmental data in an automated fashion targeted stimulation. This is optimized, in the sense that because the participating health data is mostly collected by the therapy is tailored to individual patients. The targeted physical sensors and actuators. In addition, a dedicated stimulation does not allow a medication (e.g. taking a pill) intelligent coordinator can exploit the cross-sectional data to be metabolized throughout the patient's body. Instead, consisting of IoT-provided data and clinical/genome data. it stimulates the intended target in a controlled manner, Here we present an overview of several possible avenues of and thereby reducing any side-effects. With the use of integrating the IoT with PM. medical IoT devices, it is possible to steer the stimulation to a particular target with a much higher degree of A. Risk Minimization in ADR precision [80]. As experienced in any system, the The IoT can play a significant role in mitigating the risk occurrence of medical error in healthcare in general is also associated with adverse drug reaction (ADR) [123]. In affected by a several factors. With the introduction of PM, layman's terms, an ADR is an undesirable or injurious this error margin increases exponentially because of response experienced following the administration of a modular clinical treatment approaches. For example, drug or combination of drugs under natural conditions of caregivers (e.g. hospitals) are usually at over-capacity and use and the suspicion of the unwanted response is held thus they face scalability issue to increase access to care. accountable mostly to the drug/s administrated [40]. A Co-morbidity supervision becomes even more difficult substantial number of admissions to hospital are caused by than before. To address this issue, we can establish an IoT- ADRs and hospitalized patients often experience ADRs based health network for an automatic patient caring that muddle and extend their stay. Many of the ADRs can process [81]. be avoided if the appropriate care is taken. An ADR will usually require the drug to be discontinued or the dose D. Automation in Gene Expression Measurement reduced. For example, a simple sensing system can detect Gene expression profile has widely been used to uncover whether the dose or plasma concentration has risen above the association of environmentally-swayed or disease the therapeutic range. Such a concept of an IoT-based phenotypes with the mRNA expression patterns [82]. Due ADR is found in [74]. The work makes use of barcode or to its incredible application in computable genotyping, NFC-enabled devices so that the patient side recognizes genetic variation of inter and intra organisms, early the drugs. Then, an AI-based pharmaceutical system finding of disease, polymerase chain reaction (PCR) [83] senses and analyzes the patient's health and molecular and its subsequent derivatives are widely used to obtain profiles. Eventually, the system performances matching real-time gene expression profiling. Then, because of comparison to conclude whether the suggested drug is rapid progress in miniaturized electrochemical DNA well-suited. In a normal clinical viewpoint, the nature of biosensors, it is possible to generate transformed ADR is characteristically generic i.e., not medication- electronic signal from the sensitive bio-receptor through a specific for a particular disease. Therefore, a generic transducer (e.g. photo counter) in an automatic process, software package termed ADR services is required. The calling the need of an IoT-based health network, with a ADR services is supposed to cover certain mutual minimum involvement of technical personals in the close technical issues and their generic solutions [123]. loop system. The system as a whole can eventually assist However, the ADR services to be used in PM should be PM to predict disease risks and even what foods to further customized and fine-tuned to cover the respective consume based on patients' genome and extracted PM cohort. physiological sensors data [84]. The PM basically provides customized healthcare B. Safe and Secure Medication solutions to the individual cohort of patients. With the help The safety of the medicine in PM is one of the unique of IoT, this customization itself can be improved by challenges that must be addressed by the pharmacists [79]. learning the individual's concerned physiological Also, the need for an entirely connected and transparent functions. For example, one can consider a possible way global healthcare supply chain will continue to grow and to improve the symptoms of a Parkinson's disease patient this is where the IoT can be useful. The IoT devices can through a better deep brain stimulation (DBS) therapy monitor a bunch of parameters, including location, using IoT (Figure 6). temperature, light exposure, humidity, as well as security to guard against theft and forging. Although this sort of FIGURE 6. A conceptual model of an IoT-aided personalized DBS therapy.

DBS is a neurosurgical procedure which uses a neurostimulator that delivers electrical stimulation, A. Redesign of Clinical Design Support through implanted electrodes, to specific targets in the Computer-based clinical decision support (CDS) are brain for the treatment of neuropsychiatric disorders [85]. meant to enhance the decision-making capabilities by To advance the DBS therapy, we need to understand how utilizing the individual-specific information and clinical individual's brain works. By sensing signals from the knowledge [87]. It serves to facilitate different stakeholder brain, we can learn more about how brain responds to the like physicians, nurse, patients, and others in making therapy. Low field potential (LFP) recording is promising effective clinical decisions. Formally, a CDS is referred to to enable detection, measurement, and collection of brain “a process for enhancing health-related decisions and signals [86]. The collected LFP signals along with other actions with pertinent, organized clinical knowledge and medical sensors data can then constantly be analyzed to patient information to improve health and healthcare improve the targeted DBS therapy [80]. This technology delivery” [88]. Historically, the CDSs delivered promising would eventually enable a precise adjustable algorithm results in diverse systems and services such as the which could lead a better understanding about various reminder systems, the drug dosing and drug-drug overwhelming neurological problems. interactions, the diagnoses and treatment, and the pharma- related fields [89]–[91]. Despite its potentials in E. Summary and Insight improving health and healthcare, CDS has several Digital innovations collectively appear as a paradigm challenges to accomplish its full promise [92]. Moreover, changer of how healthcare organizations provide quality the fresh developments in the medicine domain and the patient services with enhanced clinical satisfaction while presence of disruptive technologies pose a new set of maintaining a safe and secure environment at each stage challenges to develop models for CDS. This led us to put associated with the ecosystem. In line with that, IoT can question to ask, do we need to rethink about the CDS’s potentially be integrated with PM to achieve improved design in order to build a practical model for the PM era? automation in general. In particular, the IoT-enabled PM The answer is certainly positive based on the realization is potential to offer several benefits such as real-time by the researchers in their research works [8], [91], [93], monitoring of adverse drug reactions and secured where they pointed out the need of a CDS design that healthcare supply chain. Even, IoT can be utilized to encompass a more comprehensive knowledge base (KB) design innovative personalized therapies e.g., IoT-aided to fulfill the key requirements of PM. Contemporary CDSs personalized DBS therapy, conceptualized in the early part serve a fraction of clinical care whereas a common of this section. decision in PM shall require accumulating data from different components that are not integrated at one place. Researchers working in the area of informatics to advance VI. Implementation Challenges PM [8] stressed upon the designing of an all-inclusive KB In this section, we discuss more of the design challenges comprising information about disease subtypes and risks, of futuristic PM system and services such as clinical diagnosis, treatment, and prognosis. Nevertheless, the decision support systems, ecosystem, and the challenges available KBs are isolated from each other and are thus exist in in the integration and standardization of data unable to provide support for executing the federated elements and processes. queries. On that, in addition to flexibility and scalability, the KBs need to be revamped to support not only the federated queries but also an extended reasoning versions of national guidelines. The group emphasized to capability. In a study [91], authors pointed-out the data step-forward to a national implementation by designing isolation issue by highlighting the fact that the two sets of and implementing futuristic resources. data, clinical and scientific, are typically placed in We summarize the core limitations of the contemporary different repositories as information silos. They need to be CDSs in Table IV. Addressing these limitations while linked and presented in a way that clinicians and other redesigning the CDSs in the era of PM, raises to a few researchers can easily interact and review. This raises the challenges to consider for their resolution. In a study [89], requirement for a standard language and algorithm for we envisioned the conceptual architecture of the futuristic executing a federated query. The Clinical CDS eligible to support the functional requirements of PM Pharmacogenetics Implementation Consortium (CPIC) services. In contrast to contemporary architectures of Informatics Working Group is developing a standard CDSs, the futuristic CDS model incorporates the modules guidelines for the effective implementation of of supporting federated queries, a supervisor KB that Pharmacogenetics in the day-to-day medical care [94]. holds the information of disease subtypes and risks, This group also uncovered the limitation of present-day diagnosis, treatment, and prognosis. CDS issue of addressing single gene by relying on local TABLE IV LIMITATIONS OF CURRENT CDSS WITH CHALLENGES AND PROSPECT SOLUTIONS.

Current CDSs Limitations Challenges Prospect Solutions The knowledge bases of How to design a comprehensive • Allowing data sharing and consensus on clinical contemporary CDSs are isolated KBs to integrate information about interpretations and multiscale data. from one another and they are various features such as disease • Enabling effective ontological modeling, knowledge unable to support federated subtypes, disease risk, diagnosis, provenance, and maintaining the integrated KB. querying [8]. therapy, and prognosis. • Utilizing a set of novel computational reasoning approaches to allow efficient federated queries. Isolation between scientific and How to establish a meaningful • Applying standard vocabularies and data formats to clinical data [91]. connection between patient data integrate disparate data sources. and primary literature when the • Developing new research platform with a set of methods and EHR databases are considered as tools to enable analysis and visualization of not only a information silo themselves? massive amount of raw data generated in clinical set ups, but also the data resides in different databases of biomedical literature.

genes, BRCA1 and BRCA2, which currently include over B. Design of PM Ecosystem 100 research scientists and clinicians from 19 different As noted above, we briefly mentioned about the three core countries. Similarly, ClinVar [98], an archive partner of aspects of informatics (i.e., bioinformatics, participatory ClinGen project is an archive (freely available) for health informatics, and clinical informatics) with their variants’ clinical significance interpretations. National basics and provided information on selected set of tools Center for Biotechnology Information (NCBI) has and techniques. Nevertheless, it is also important to provided an explorer tool to facilitate identification of discuss the informatics solutions for PM which requires a clinical significance discrepancies in ClinVar [99]. In one holistic overview of working together as outlined below: of the reports [11] there is given a set of fundamental • Curation of data generated via participatory health aspects of PM and describes the key aspects of using mobile devices, sensors, social media, and other computational infrastructure built on clinical-grade IoT devices as well as environmental factors’ data at genomic sequencing. Authors therein emphasized on the a point of care for the assistance of genomic data integration of PM program into a medical institution’s interpretation which ultimately could help in precise clinical system to facilitate billing and reimbursement. patient care. The proposed PM infrastructure integration with existing • Creating a synergy between bioinformatics and electronic health record infrastructure is shown in Figure clinical informatics by developing infrastructure, 7. The existing EHR infrastructure depicted on the left is tools, techniques and applications that bridge the two integrated with PM infrastructure on the right through areas and allowing the sharing of data to offer passing the patient specimen information to the laboratory integration of individual patient data into the clinical information management system (LIMS) in order to research environment [95]. process, sequence, and analyze the specimen data. The • Development of a comprehensive framework that LIMS component of the PM infrastructure sends back the facilitates tools and techniques to integrate, process, report formed over the specimen data to the pathology and analyze data curated from diverse sources in all system of the EHR infrastructure. three areas; clinical, genomic, and lifestyle & environmental factors to enable one-point decision in C. Integration and Standardization a precise manner. For successful data integration and exchange, data and • Development of a coherent framework for dealing metadata standards are required. However, there are with multi-scale population data including the several issues to achieve this goal in terms of either phenome, the genome, the exposome, and their lacking of such standards or inconsistent use of existing interconnections [96]. standards, particularly in “omics” domain [8]. Prior to A series of efforts have been made to provide informatics frame these issues for discussion, we first describe the solution to support PM in a comprehensive manner. For meanings of what constitutes a ‘data standard’ in order to instance, the network ENIGMA (Evidence-based network avoid confusion as different groups and individuals have for the interpretation of germline mutant alleles) [97] is an different definitions for standards. According to the international consortium for assessing clinical International Organization for Standardization, a standard significance and risk related to sequence variation in is, ‘… a document that provides requirements,

FIGURE 7. Components of an integrated precision medicine workflow illustrating existing HER and novel PM infrastructure- (QC: Quality Control) specifications, guidelines or characteristics that can be Expression’ appeared in the form of Object Model used consistently to ensure that materials, products, (MAGE-OM), Markup Language (MAGE-ML) [106], processes and services are fit for their purpose’ [99]. and a Controlled Vocabulary called the MGED Ontology There is further division in standards as data standards for [107]. These standards resulted the creation and evolution integration and exchange and data standards for security, of a several interoperable databases and repositories [105]. privacy, and integrity, covered as one of seven key areas in the research work [8]. The research emphasized on the For seamless integration and information exchange, need of extending the scope of the existing standards recently Health Level Seven (HL7) extended the efforts on rather to invent a new. For that, the individuals or Genomic data working group with its newly invented organization seek to adopt an existing standard should popular standard called Fast Health Interoperability work closely with the owner of that standards to extend Resource (FHIR) [108], [109]. One of the main FHIR the scope. Not only co-working, relevant stakeholders resources is a Sequence resource which is designed to should focus on outreach and education/training to describe an atomic sequence containing the alignment educate the potential adopters of understanding and using sequencing test result and multiple variations [110]. For existing data standards. Data standardization for the facilitation of standardized clinic-genomics apps, a integration and exchange is required for correct framework called Substitutable Medical Applications & interpretation of the data elements. The motivation for Reusable Technologies (SMART) on FHIR Genomics is data standardization on security and privacy comes from developed which specifies genomic variant data resource the notion of developing mutual consensus on the level of definitions [111]. SMART on FHIR Genomics data as well as the protocol definition for sharing. specification offers developers a unified framework to work with multiple resources of genomic and clinical data A number of initiatives have been taken place to facilitate to facilitate the type of apps required for precision the adoption of data standards especially in the ‘omics’ medicine. A brief summary of pertinent initiatives on discipline. One of these initiatives is BioSharing that standardization in the area of PM is provided in Table V. works to ensure the standards are searchable and informative by mapping the landscape of community TABLE V CLASSIFICATION OF STANDARD INITIATIVES IN THE DOMAIN OF PRECISION MEDICINE developed standards in the life sciences including biomedical sciences [100]. BioSharing facilitates those Initiative Year Standard Body Scope who are looking for information based on the existing Name standards, finding duplications or gaps, encourage Biosharing 2011 FAIRsharing harmonization to avoid reinvention, and developing [100] team, Oxford criteria items for evaluating standards for adoption. The e-Research American College of Medical Genetics and Genomics Center. (ACMG) together with Association of Molecular MIAME 2001 FGED- Pathology (AMP) and College of American Pathologists [104] Functional Integration members formed a workgroup with the goal of developing Genomics and Exchange a classification of sequence variants using criteria Data Society informed by expert opinion and empirical data [101]. This SMART 2015 HL7 ® workgroup is aimed at providing detailed variant on FHIR International classification guidance to update the recommendations on Genomics interpretations of sequence variants previously provided [109] by the ACMG. The Canadian Open Genetics Repository PMI Data - ONC – Office is an endeavor aims to establish collaboration of Canadian Security of the National laboratories with other countries to support the Principles Coordinator development of tools for sharing laboratory data in Guide for Health Privacy and addition to the collection, storage, sharing and robust [112][113] Information Security analysis of variants in the laboratories across Canada Technology [102]. There are other initiatives have been established to PMI- 2016 GSWG ensure data security and privacy through standardization. AURP For instance, a framework is established that provides [114] guidance for responsible sharing of genomic and health- HGNC 2007 HUGO related data including personal data [103]. The Minimum database Information About a Microarray Experiment (MIAME) [115] Vocabulary [104] provides guidelines for the minimum information to UMLS ® - U.S. National and/or describe the experiment details of DNA microarray data [116] Library of Nomenclature so that the experiment could either be reproduced or Medicine analyzed the data de novo [105]. Similarly, data modeling (NIH) and XML-based exchange standards ‘Microarray Gene

D. Summary and Insight Precision Medicine is included as part of China’s five-year At a granular detailed level, there exist a lot of plan with an expected investment of more than $9 billion implementation challenges that are highlighted in various for research. Among 40 countries where there are studies ranging from genotype data preprocessing, initiatives related to PM, China is on the top from mapping and alignments, unstructured clinical text investment perspective. Compared to the United States processing, image processing and environmental data PMI investments, China is spending $43 for every $1 of acquisition and synchronization. In this section, we US, thus making China as a global leader in PM [123]. The focused on generalized implementation challenges that Beijing Genome Institute (BGI) [124] is the world’s exist irrespective of individuals' realizations of PM. The largest genomic organization with a focus of genetic challenges are mostly related to the rethinking on a new sequencing. Affiliated to BGI, the China National design for the clinical decision support systems to include GeneBank [125] has over 500 million genetic sequences information from the other aspects of PM – molecular, - stored in more than 40 databases, as of early 2017. Sichuan omic and environmental in order to produce the right University’s West China Hospital which is ranked the first decision for the right patient. We included the designs in among all Chinese hospitals for four consecutive years in the existing studies and provided the abstract science & technology influence, plans to sequence 1 representation of an ecosystem of PM for enhancing the million human genomes itself [126], [127]. AliCloud by design of existing electronic health record systems with Alibaba Group partnered with BGI and Intel Corporations genome. We deliberated the integration challenges at data have launched Asia’s first cloud platform for precision and process levels and the standardization efforts at global medicine and its applications with a vision to accelerate spectrum. the advent of precision medicine [128]. More to PM initiative, it is anticipated that leading institutes, including VII. PM Global Initiatives Fudan University, Tsinghua University, and the Chinese PM is spreading globally with a fast pace and is thereby Academy of Medical Sciences, are trying to establish creating a multibillion market. According to a report precision-medicine centres [126]. issued by Persistence Market Research, global PM market is expected to approach $ 172.95 Billion by the end of 2024 [117], [118]. Different countries share this market by C. United Kingdom United Kingdom has started a well-known project initiating innovative projects to support PM in terms of ‘100,000 Genomes Project’ in 2013 with a goal to establishing infrastructure, research centers, working sequence 100,000 genomes from around 70, 000 people groups, and standardization bodies. Different countries from the participants of National Health Service (NHS) allocated different sort and amount of funding to support patients having a rare disease [129][130]. This project is the PM initiative. In this section, we briefly elaborate considered currently as the world’s largest national country-wise initiatives with their goals and way of sequencing project of its kind. A program Coordination working. Group led by Innovative UK is active in precision medicine to bring together representatives from UK public A. United States sector and charity funders and through this group, a dataset United States of America took the lead launching the idea of over 400 infrastructure investments in precision of Precision Medicine under the Obama administration medicine has already been developed [131], [132]. back in 2015 [1]. The idea was taken further by the Innovate UK is envisioned to invest up to £6 million in National Institutes of Health (NIH) and other partners. precision medicine technologies related innovation Initially, a budget of $215 million investment has been projects [133]. Overall, the UK government has invested announced in the President’s 2016 Budget. NIH-funded more than £1 billion in developing precision medicine resource ClinGen is dedicated to constructing an research infrastructure [134]. authoritative central resource to establish clinical relevance of genes and variants for the convenient use in precision medicine and research [119][120]. Ensuring the D. Japan Like other countries, Japan is also contributing to support accuracy of NGS tests, US FDA is working on three the development of personalized and precision medicine aspects; guidance for Databases to allow developers to use (PPM) [135]. Japan has established three biobanks to data from FDA-based databases of genetic variants, collect genome data; Bio Bank Japan, National Center Bio recommendations for designing, developing, and Bank Network, and Tohoku Medical Megabank. All these validating NGS tests, and support to develop three banks work together, however Bio Bank Japan being bioinformatics tool to engage users across the world to the largest of the thee, plans to collect data from 300,000 experiment, share data, and test new approaches [121]. people alone. The total budget of $ 103 million is allocated PrecisionFDA [122] as a community platform for NGS in 2016 for the plans like clinical trials, research on assay evaluation and regulatory science exploration, has genomic care, and establishing seven core hospitals to resulted as an outcome of FDA efforts. support the provision of genomic medical treatments [135]. Additionally, Japan has established the most B. China successful National Cancer Genome Screening System (SCRUM-Japan) project under the supervision of National with other associated countries and stakeholders, have Cancer Center Hospital, Japan [136], which assists developed a European strategy framework for hospitals and pharmaceutical companies develop PPM for personalized medicine [6]. PerMed [148] is Coordination cancer. The aim of SCRUM-Japan trials is to enroll 4750 and Support Action (CSA) of 27 partners including patients with cancer in about 2 years’ plan starting in European key stakeholders and decision makers to allow February 2015 and ending in March 2017 [136]. synergies, avoid duplication, and ensure maximum transparency preparing Europe for leading the global way E. South Korea [149]. The International Consortium for Personalized South Korea introduced itself with the International Medicine (ICPerMed) is a voluntary, EU Member states- Precision Medicine Center (IPMC) as the world’s first led collaboration that brings together over 30 European Precision Medicine center focused on Cell Therapy. The and international partners to work on coordinating and IPMC is envisioned to take a pioneering role in fostering research to develop and evaluate personalized standardization of future medicine with a focus on genome medicine [150][6]. and bio convergence technology [137]. The Korean scientists are succeeded to produce a de novo genome G. Australia assembly for a Korean individuals and the results are Australia perhaps the world’s first country having center published in Nature [138]. The Korea’s biobanking specializing in precision medicine for infants and your system is currently operating a nationwide network of 17 children which is funded at Murdoch University and have university-affiliated hospitals to collect bio-specimens received $473,000 in funding from the WA Department of from patients and then National Biobank of Korea has Health [151]. Precision medicine has the potential to collected biological samples from some 770, 000 people transform Australia’s health care system as described in a and distributed them to 1,915 research projects which report released by the Australian Council of Learning resulted in a total of 751 research papers as of the end of Academies (ACOLA) [152]. ACOLA has started a project December 2016 [139], [140]. From industrial sector, the on precision medicine with a goal to explore the current information erupted that the Syapse – a leading precision trends in precision medicine technologies and a broader medicine company joined hands with Seoul National implementation in the Australian context. In the ACOLA University Hospital (SNUH), Korea to launch a precision detailed report, there are 12 potential areas are highlighted oncology program for cancer care improvement in Korea where precision medicine is likely to show significant [141]. Moreover, the Korean National Cancer Center with impact in the next five to ten years [153]. Australian the U.S. National Institutes of Health announced to Genomics is a national network of clinicians, researchers, establish a large-scale precision medicine cohort on cancer and diagnostic geneticists and is made up of more than 70 [142]. In summary, the Korean government plan is to partners organizations with a vision to integrate genomic invest $55.7 million in Precision Medicine until 2021 medicine into healthcare across Australia [154]. National [143]. Health and Medical Research Council’s (NHMRC) awarded a $25 million grant in 2015 to Australian F. Europe Genomics for a targeted Call for Research into Preparing European Union (EU) is put forwarding numerous efforts Australia for the Genomics Revolution in Healthcare. to promote precision medicine in the Europe region. As the world’s biggest public-private partnership between EU Precision Medicine is largely endorsed by other parts of and the European pharmaceutical industry, the Innovative the world such as African, Middle East, and others Asian Medicine Initiative (IMI) facilitates collaborations countries in their own capacity and scope. Orion Health between the stakeholders and provides grants and other Canada, for instance, has developed a care coordination financial support to major research projects [144]. IMI in tool that allows patients to digitally create, update and phase 2 that is IMI 2 program (2014-2020), will get a total share their personalized care plan as well as the clinicians budget of €3.276 billion, of which €1 billion came from are provided with the cognitive support to make the best the Health theme of the EU's Seventh Framework Program decisions possible [155][153]. Similarly, the Precision for Research (FP7) and €1 billion came from in-kind Driven Health initiative (PDHI) in New Zealand is contributions by EFPIA companies [145]. According to a contributing to the growing body of international research report by ZION, Europe precision medicine market is to enable the practice of the precision medicine while expected to reach approximately USD 72,800.0 Million by including genetic data, as well as information from 2022 [146]. Under EU’s Horizon2020 Program, exogenous sources such as an individual's diet and social Barcelona has started European three-dimensional (3D) circumstances [156]. genomics project “Multi-scale complex genomics” with a A wide array of international initiatives and consortiums goal is to standardize experiments in 3D genomics and have established to form guidelines for the responsible and relevant activities like storage of data. The project is homogeneous approach to data movement from one place allocated a budget of €3 million and will be conducted to another place [5]. For instance, the Global Alliance for over three years [147]. The EU funded project “PerMed”, where representatives from EU Member States together Genomic and Health (GA4GH) is meant to create Integration of heterogeneous data types challenge. The interoperable technical standards [157]. numerous data types such as omics, molecular, imaging, pathology, physiology, lifestyle, and clinical will be VIII. Future Directions required to incorporated together for predictive models PM is broadly welcomed around the world; the area is [162]. The orthogonal nature of molecular assays does not however still in its infancy and many aspects are allow smooth analysis with clinical data, as a result, untouched and mount of challenges lie ahead. It is still a separate analysis is performed initially and later they are big challenge to construct an infrastructure that entirely integrated. This kind of practice is time consuming and it supports the prevalent sharing and effective use of health hides the holistic view of data at one place. and genomic data in order to advance the healthcare system that is least reliant on on external sponsored Data privacy challenge. The protection of genomic data resources [30]. from being used against employment and health protection, various ethical and social issues need to be A. Challenges and open questions addressed [163]. It is also required to educate public Data complexity, volume, and computational challenges. The workforce, develop human capital and infrastructure, and computational requirement of molecular and -omics data empower the general public with correct information. analysis is huge. The big data analytics is challenging Moreover, Cloud and Web is likely to play a huge role in because of multiple factors such as frequency, quality, the management of massive genomic data and the same dimensionality, and heterogeneity [29]. The processing time, mobile computing will be used to access those data power and memory of personal computers are usually not which increases the privacy concerns [164]. enough to process DNA sequence data for analysis and interpretations. To support individual researchers for their B. A proposed holistic integrated precision medicine investigations, need cloud-based computing resources to framework share the processing power and space. The biomedical data complexity upsurges in dual directions: the number of sample and the heterogeneity [12]. These voluminous To address the unresolved challenges of PM, more complex data are available in different regions of the informatics approaches are required to be designed. We world through different initiatives using -omic and designed a futuristic framework (Figure 8), by molecular data capturing technologies which are now incorporating functions covering the most needed areas of becoming faster and cheaper. The variety of available PM implementation. The framework is a high-level biological data entities for instance genes, proteins, demonstration of modules such as primary analysis, metabolites, drugs, diseases, etc. are so large to manage secondary analysis connected with knowledge through basic and simple methods. To handle, process, management and data analytics that produce knowledge and annotate such a gigantic and diverse data is not only and data services respectively. These services are computationally intensive rather it requires significant provisioned to use by different stakeholders and computational hardware [158]. For instance, mapping of organizations including hospitals, pharmacies, and short reads to get 30x coverage of the human genome, laboratories. The framework has also provision for require 13 CPU days. Not only hardware, rather a security and privacy functions to access to the individuals’ comprehensive database that contains clinical, genomic data through adequate authentication, authorization, and and molecular information as much as possible. To deal access policy. with the high-throughput data, various method for The primary analysis module is designed to acquire dimensionality reduction in feature extraction (PCA, diverse data from different input sources: clinical data, SVD, tensor-based approaches [159]) and in feature molecular and -omic data, sensory data, environmental selection (filter-based and wrapper-based sequential data, and published literature data. At this stage, the data feature selection [160]) are experimented. is preprocessed to filter-out the undesirable data items Creation of mutation databases challenge. Knowledge through the application of different natural language bases for example ClinVar and MyCancerGenome are still preprocessing and other statistical techniques. The immature and unfinished thus raises the need to create primary analysis module utilizes the support of multiple custom mutation databases by different centers [11]. Also, tools particularly, -omic data preprocessing tools such as there is a lack of precise annotations of variants which GMAP, BWA, GATK, and others. required databases to contain the curated variants and their interactions with potential drugs [161].

FIGURE 8. A holistic integrated precision medicine framework

The secondary analysis module analyzes the data received for both the informatics community and the medical as an outcome from the primary analysis. Some parts of community to collaborate with each other to make a the data need to be integrated for combine analysis and combine effort for achieving the common goal of a better- other may be analyzed independently. One of the quality patient care. In this study, we elaborated the major important activities is to find correlations among various areas of research and development for the realization of data items such as among the genes as how they are related PM in the perspective of informatics. The study provides while studying a disease occurrence due the genes a fair attention to cover the important aspects and mutations. Similarly, it takes care of the correlation requirements to establish the PM program. We explained between genotypes and phenotypes to study the the need of coexistence of EBM and PM by bridging the relationships of clinical factors and gene mutations. The gap between them. To understand the informatics analyzed data is stored as an internal storage for further viewpoint of how the PM is implemented, we provided an processing as well as it is provided to the external entities overview of enabling tools and techniques in three as a service. potential areas: biomedical informatics, clinical informatics, and participatory health informatics. For a On the top of secondary analysis, there are two modules: deeper understanding of PM, the paper offers a broad view knowledge management and Data Analytics. Both on how AI and big data become an integral part of PM. modules utilize the analyzed data generated at the We also associated the IoT paradigm with PM and secondary analysis. The knowledge management module uncovers various advantages of integrating the two constructs KBs by creating, maintaining, and validating approaches. In addition, this paper highlights some of the knowledge rules from the analyzed data. Based on this major implementation challenges in terms of knowledge, various knowledge services such clinical computational tools, data integration, security, decision support services can be produced. Similarly, the standardization, and overall infrastructural solutions that data analytics module targets to design models for are required to implement PM. Finally, we proposed an descriptive, predictive, and prescriptive services. The integrated holistic framework for PM to overcome the analytical models generate data visualization services to existing limitations. In summary, the outcomes of this present data in graphs, charts, and other statistical mode of study are expected to be beneficial for the researchers and presentations. professionals working in the area of medical informatics. IX. Conclusions Conflicts of Interest: The authors declare no conflict of Both medical professionals and informatics researchers interest. across the globe have started to device computational infrastructural solutions to address the need of timely and precise decision on a patient health issue. It is a high time References 525–527, 2015. [1] F. S. Collins and H. Varmus, “A New Initiative on Precision Medicine,” N. Engl. J. Med., vol. 372, no. 9, pp. 793–795, [19] N. Chow, L. Gallo, and J. W. Busse, “Evidence-based Feb. 2015. medicine and precision medicine: Complementary approaches to clinical decision-making,” Precis. Clin. Med., [2] N. Servant et al., “Bioinformatics for precision medicine in vol. 1, no. 2, pp. 60–64, Sep. 2018. oncology: principles and application to the SHIVA clinical trial,” Front. Genet., vol. 5, p. 152, May 2014. [20] J. De Leon, “Evidence-based medicine versus personalized medicine: Are they enemies?,” J. Clin. Psychopharmacol., [3] G. W. Canonica et al., “Asthma: personalized and precision vol. 32, no. 2, pp. 153–164, Apr. 2012. medicine,” Curr. Opin. Allergy Clin. Immunol., vol. 18, no. 1, pp. 51–58, Feb. 2018. [21] “The relationship between evidence-based and data-driven medicine | CIO.” [Online]. Available: [4] I. R. König, O. Fuchs, G. Hansen, E. Von Mutius, and M. V https://www.cio.com/article/3235025/healthcare/the- Kopp, “What is precision medicine? REVIEW PRECISION relationship-between-evidence-based-and-data-driven- MEDICINE,” Eur Respir J, vol. 50, p. 1700391, 2017. medicine.html. [Accessed: 28-Dec-2017].

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