Clinical Decision Support Systems: A Visual Survey

Clinical Decision Support Systems (CDSS) form an important area of research. In spite of its importance, it is difficult for researchers to evaluate the domain primarily because of a considerable spread of relevant literature in interdisciplinary domains. Previous surveys of CDSS have examined the domain from the perspective of individual disciplines. However, to the best of our knowledge, no visual scientometric survey of CDSS has previously been conducted which provides a broader spectrum of the domain with a horizon covering multiple disciplines. While traditional systematic literature surveys focus on analyzing literature using arbitrary results, visual surveys allow for the analysis of domains by using complex network-based analytical models. In this paper, we present a detailed visual survey of CDSS literature using important papers selected from highly cited sources in the Thomson Reuters web of science. We analyze the entire set of relevant literature indexed in the Web of Science database. Our key results include the discovery of the articles which have served as key turning points in literature. Additionally, we have identified highly cited authors and the key country of origin of top publications. We also present the Universities with the strongest citation bursts. Finally, our network analysis has also identified the key journals and subject categories both in terms of centrality and frequency. It is our belief that this paper will thus serve as an important role for researchers as well as clinical practitioners interested in identifying key literature and resources in the domain of clinical decision support.


Introduction
The study of Clinical decision support systems (CDSS) constitutes a significant field of usage of information technology in healthcare.CDSS are designed to assist clinicians and other healthcare professionals in diagnosis as well as decision-making.CDSS uses healthcare data and a patient's medical history to make recommendations.By using a predefined set of rules, CDSS intelligently filters knowledge from complex data and presents at an appropriate time (Osheroff and Association 2006).By adopting CDSS, healthcare can become more accessible to large populations.However, it also implies that at times, CDSS may be used by people having literal medical knowledge (Ahn, Park et al. 2014).
Several researchers have contributed in the form of systematic literature reviews (SLR) and surveys to provide readers with insightful information about CDSS, as demonstrated below in Table 1.

Background
This section presents the necessary background of Decision Support System and CDSS.

Decision Support System (DSS)
The idea of DSS is very broad and different authors have defined it differently based on their research and roles DSS plays in the decision-making process (Druzdzel andFlynn 1999, Holsapple 2008).Some people regard DSS as a field of information management systems, whereas others consider it as an extension of management science systems (Keen 1980).Keen in his paper (Keen 1980) states that "there can be no definition of Decision Support Systems, only of Decision Support".Authors of (Finlay 1994) define it as "a computer-based system that aids the process of decisionmaking".Whereas the authors of (Turban 1990) define it as "an interactive, flexible, and adaptable computer-based information system, especially developed for supporting the solution of a nonstructured management problem for improved decision-making.It utilises data, provides an easy-touse interface, and allows for the decision maker's own insights."For further details, we encourage interested readers to see (Marakas , Ralph, Sprague et al. 1986, Silver 1991, Power 1997, Sauter 1997, Schroff 1998, Druzdzel and Flynn 1999, Power 2000, Power 2002).

History
The notion of DSS has evolved in the late 1950s, from the theoretical studies of organisational decision-making and in the early 1960s from technical work on interactive computer systems (Keen and Scott 1978).The idea of assisting decision-makers using computers was published in 1963 (Bonini 1963).Scot Morton is known as one of the first researcher's groups who coined the term DSS (Scott 1971).Research on DSS has gained momentum in 1974, and by 1979 nearly 30 case studies in the domain of DSS have been published (Keen 1980).Almost 271 applications of DSS have been published during the time span of May 1988 to 1994 (Eom, Lee et al. 1998).

Architecture
Again, the architecture of DSS varies because different researchers have identified different components in DSS, e.g.(Sprague Jr and Carlson 1982, Haettenschwiler 2001, Power 2002).However, (Marakas) identifies five fundamental components of a generic DSS architecture: i) the user, ii) the data management system, iii) the knowledge engine, iv) the model management system, and v) the user interface.

Classification
Once again, there is no universal classification of DSS; different researchers have proposed a different classification.Based on user criterion, authors classify as passive DSS, active DSS, and cooperative DSS (Haettenschwiler 2001).Whereas, based on the conceptual criterion, authors classify as data-driven, knowledge-driven, communication driven, model-driven DSS, and document-driven (Power 2002).
We intend to provide insight to CDSS researchers and practitioners about historical trends, current developments, and future directions of the CDSS domain.

Types
There exist two main types of CDSS.The first one is derived from expert systems and uses knowledge base.The knowledge base depends on inference engine to implement the rules, such as ifthen-else on the patient data and presents the findings to end-users [2].The second type of CDSS is based on the non-knowledge based systems, which depends on machine learning techniques for the analysis of clinical based data (Alther and Reddy 2015).The architectural parts in the conventional structures of CDSS consist of; user, knowledge base, inference engine and user interface (Bonney 2011).

Benefits
The key benefits of CDSS reported in the studies conducted in (Ivbijaro, Kolkiewicz et al. 2008, Haynes and Wilczynski 2010, Kawamoto, Del Fiol et al. 2010, Wright, Sittig et al. 2011, Musen, Middleton et al. 2014) are as follows: 1.Higher standards of patient safety: CDSSs have helped healthcare organisations all over the world acquiring higher standards of patient safety by adopting standardised clinical procedures governed by the clinical workflows encoded through these systems.Thus reducing diagnostic and prescribing errors and drug doubling issues.2. Improving the quality of direct patient care: Their research also concluded that with the advent of CDSS, quality of care has improved to considerable levels with this extra support provided to clinicians (who are already struggling to cope with current healthcare demands).This has made it possible for clinical experts to allocate more time in providing direct patient care.3. Standardisation and conformance of care using clinical practice guidelines: The standardisation of clinical pathways and procedures set precedents and evaluation benchmarks for healthcare trusts to achieve higher patient satisfaction levels set out by different healthcare organisations in different regions.CDSS also promote the utilisation of clinical practice guidelines (CPGs) for the development of knowledge-aware systems capable of performing effective clinical decisionmaking to promote standardised care.4. Collaborative decision-making: CDSS have helped healthcare stakeholders that include clinicians, healthcare trusts and policy makers to develop safe and efficient care models using a collaborative decision-making approach to benefit both patient and a clinician.CDSS have also helped healthcare trusts to improve effectiveness in the prescribing facility through cost-effective drug order dispensation (Wright, Sittig et al. 2011).CDSS are also playing an important role in the integration of EHRs, which will help healthcare authorities to streamline information collection and clinical diagnosis operations in order to promote efficient data gathering (Ivbijaro, Kolkiewicz et al. 2008).The audit trail is another important aspect of modern healthcare systems which is achieved through the intelligent exploitation of clinical decision support capabilities.

Existing Reviews
Many reviews have identified the benefits of the CDSSs, in particular, Computerized Physician Order Entry systems (Hunt, Haynes et al. 1998, Eslami, de Keizer et al. 2008, Zuccotti, Maloney et al. 2014).The CDSS as part of the Computerized Physician Order Entry has been found to alleviate adverse drug events and medication errors (Jaspers, Smeulers et al. 2011, Steinman, Handler et al. 2011, Bright, Wong et al. 2012).CDSSs also have demonstrated to improve clinician performance, by way of promoting the electronic prescription of drugs, adherence to guidelines and to an extent the efficient use of time (Jaspers, Smeulers et al. 2011, Bright, Wong et al. 2012).CDSSs perform a key role in providing primary care and preventative measures at outpatient clinics, e.g. by alerting caregivers of the need for routine blood pressure checking, to recommend cervical screening, and to offer influenza vaccination (Hunt, Haynes et al. 1998, Ahmadian, van Engen-Verheul et al. 2011).
To provide effective healthcare delivery to patients, CDSS is used both in primary and secondary care units.In order to take maximum advantage from cardiovascular CDSS, it is required to ensure clinical governance in the next-generation clinical systems by considering a strong foundation in wellestablished clinical practice guidelines and evidence based medicine (Farooq and Hussain 2016).

CDSS Adoption
The adoption of CDSSs in diagnosis and management of chronic diseases, such as diabetes (O'Connor, Sperl-Hillen et al. 2011), cancer (Clauser, Wagner et al. 2011), dementia (Lindgren 2011), heart disease (DeBusk, Houston-Miller et al. 2010), and hypertension (Luitjes, Wouters et al. 2010) have played significant clinical roles in the main health care organisations in the improvement of clinical outcomes of the organisations worldwide at primary and secondary care.These CDSS also provide the foundation to system developer and knowledge expert to collate and build domain expert knowledge for screening by clinicians and clinical risk assessment (Khong andRen 2011, Wright, Sittig et al. 2011).
An alternate approach to computer-assisted decision support was provided in the MYCIN development program, a clinical consultation system that de-emphasised diagnosis to concentrate on the appropriate management of patients who have infections (Shortliffe 1986).

Applications
CDSSs are considered as an important part in the modern units of healthcare organisations.They facilitate the patients, clinicians and healthcare stakeholders by providing patient-centric information and expert clinical knowledge (Classen, Phansalkar et al. 2011).To improve the efficiency and quality of healthcare, the clinical decision-making uses knowledge obtained from these smart clinical systems.The Automated DSSs of Cardiovascular are available in primary health care units and hospital in order to fulfil the ever-increasing clinical requirements of prognosis in the domain of coronary and cardiovascular diseases.The computer-based decision support strategies have already been implemented in various fields of cardiovascular care (Kuperman, Bobb et al. 2007).In the US and the UK, these applications are considered as the fundamental components of the clinical informatics infrastructures.
Ontology-driven DSS are being used widely in the clinical risk assessment of chronic diseases.The ontology-driven clinical decision support (CDS) framework for handling comorbidities in (Abidi, Cox et al. 2012) presented remarkable results in the disease management and risk assessment of breast cancer patients, which was deployed as a CDSS handling comorbidities in the healthcare setting for primary care clinicians in the Canada.They utilised semantic web techniques to model the clinical practice guidelines which were encoded in the form of a set of rules (through a domain-specific ontology) utilised by CDSSs for generating patient-specific recommendations.
Matt-MouleyBoumrane from the "University of Glasgow, 'UK" implemented an ontology-driven approach to the development of CDSS in the pre-operative risk assessment domain.In (Bouamrane, Rector et al. 2009), they reported their work by combining a preventative care software system in the pre-operative risk assessment domain with a decision support ontology developed with a logic based knowledge representation formalism.In (Farooq, Hussain et al. 2011, Farooq, Hussain et al. 2012, Farooq, Hussain et al. 2012), authors demonstrated utilisation of ontology and machine learning inspired techniques for the development of a hybrid CDS framework for cardiovascular preventative care.Their proposed CDS framework could be utilised for automatically conducting patient pre-visit interviews.Rather than replacing human experts, it would be used to prepare the patients before visiting a hospital, deliver educational materials, preorder appropriate tests, cardiac risk assessment scores, heart disease and cardiac chest pain scores.It would make better use of both patient and clinician time.
The ontology-driven recommendation and clinical risk assessment system could be used as a triage system in the cardiovascular preventative care which could help clinicians prioritize patient appointments after reviewing snapshot of patient's medical history (collected through an ontologydriven intelligent context-aware information collection using standardised clinical questionnaires) containing patient demographics information, cardiac risk scores, cardiac chest pain and heart disease risk scores, recommended lab tests and medication details.In (Farooq and Hussain 2016), they also have validated the proposed novel ontology and machine learning driven hybrid CDS framework in other application areas.

Methodology
In Figure 1, we illustrate the proposed methodology for the visual analysis of bibliographic literature in the domain of CDSS to uncover emerging patterns and trends.

3.1.
Data Collection The input dataset was collected from the Thomson Reuters Web of Science (Reuters 2008) between the timespan of 2005 to 2016.Data was retrieved on 11 Nov 2016, by an extended topic search for CDSSs including the web of science.The databases searched included SCI-Expanded, SSCI, and A&HCI.The search was confined to document types including articles, reviews, letters, and editorial material published in the English language.Each data record includes information as titles, authors, abstracts, and references.The input dataset contains a total of 1,945 records.
It is pertinent to note here that there is a problem in data collected from Web of Science.The WoS data identified two cited-authors named as "Anonymous" and "Institute of Medicine."In terms of frequency, Anonymous is the landmark node.However, on searching online it is found that WoS has picked it based on terms.Whereas on an extensive search on the internet, we found multiple papers having "Institute of Medicine" as an author.

3.2.
CiteSpace: An Overview In this research, we have used CiteSpace a key visually analytical tool for information visualisation (Chen 2006).CiteSpace is custom designed for visual analysis of citations.It uses colour coding to capture some details, which otherwise cannot be captured easily by using any other tool.In CiteSpace users can specify the years' range and the length of the time slice interval to build various networks.CiteSpace is based on network analysis and visualisation.It enables interactive visual analysis of a knowledge domain in different ways.By selecting display of visual attributes and different parameters, a network can be viewed in a variety of ways.CiteSpace has been used to analyse diverse domain areas such as agent-based computing (Niazi and Hussain 2011), cloud computing (Wu and Chen 2012), cross-language information retrieval (Rongying and Rui 2011), and clinical evidence (Chen and Chen 2005).
One of the key features of CiteSpace is the calculation of betweenness centrality (Chen 2006).The betweenness centrality score can be a useful indicator of showing how different clusters are connected (Chen 2016).In CiteSpace, the range of betweenness centrality scores is [0, 1].Nodes which have high betweenness centrality are emphasised with purple trims.The thickness of the purple trims represents the strength of the betweenness centrality.The thicker the purple trim, the higher the betweenness centrality.A pink ring around the node indicates centrality >= 0.1.
Burst identifies emergent interest in a domain exhibited by the surge of citations (Niazi and Hussain 2011).Citation bursts indicate the most active area of the research (Chen 2016).Burst nodes appear as a red circle around the node.

Colours Used
CiteSpace is designed for visualisation; it extensively relies on colours, therefore the description in this paper is based on colours.
The colours of the co-citation links personify the time slice of the study period of the first appearance of the co-citation link.Table 2demonstrates CiteSpace's use of colour to visualise time slices.Blue colour is for earliest years, the green colour is for the middle years, and orange and red colours are for the most recent years.A darker shade of the samecolour corresponds to earlier timeslice, whereas lighter shades correspond to the later time slice.

Node Types
The importance of a node can be identified easily by analysing the topological layout of the network.Three most common nodes, which are helpful in the identification of potentially important manuscripts are i) hub node, ii) landmark node, and iii) pivot node (Chen 2006).
Landmark nodes are the largest and most highly cited nodes.In CiteSpace, they are represented by concentric circles with largest radii.The concentric citation tree rings identify the citation history of an author.The colour of the citation ring represents citations in a single time slice.The thickness of a ring represents the number of citations in a particular time slice.
Hub nodes are the nodes with a large degree of co-citations.Pivot nodes are links between different clusters in the networks from different time intervals.They are either gateway nodes or shared by two networks.Whereas turning points refer to the articles which domain experts have already identified as revolutionary articles in the domain.It is a node which connects different clusters by same coloured links.

Results and Discussion
This section briefly demonstrates results of our analysis.

4.1.
Identification of the Largest Clusters in Document Co-Citation Network To identify the most important areas of research, here we used cluster analysis.CiteSpace is used to form the clusters.It uses time slice to analyse the clusters.The merged network of cited references is partitioned into some major clusters of articles.In Figure 2, years from 2005 to 2016 show up as yearly slices represented by unique colours.We have selected top 50 cited references per one-year time slice.The links between the nodes also represent the particular time slices.In (Chen 2006) authors noted clusters with same colours are indicative of co-citations in any given time slice.The cluster labels start from 0; the largest cluster is labelled as (#0), the second largest is labelled as (#1), and so on.The largest cluster is the indicator of the major area of research.
It can also be noticed in the Fig. 2 that the articles of David W. Bates (1999) and Thomas D. Stamos ( 2001) are the intellectual turning points, which join two linked clusters: (cluster #4) "combination" and (cluster #12) "family practice" respectively.Similarly, articles of Heleen Van DerSijs ( 2008) and Blackford Middleton (2013) are the intellectual turning points, which join two linked clusters: (cluster #2) "decision support" and (cluster #16) "computerised prescriber order entry" respectively.After a gap of five years, Middleton B has cited a paper of Van DerSijs H, which drew the interest of many researchers in the field of "decision support".In Table 3, details of top five co-cited references are given in terms of high frequency.By observing this table, we observed that the top five articles have low centrality, but are still significant by having more frequency.

It is interesting to note that the half-life of the article of
The article by Amit X. Garg ( 2005) has the highest frequency of citations among all the cited references.Following it are the articles of Kensaku Kawamoto and Gilad J. Kuperman published in 2005 and2007 respectively.The articles of Van DerSijs H and Basit Chaudhry are also included in the top five articles of this domain.The merged network contains a total of 611 cited references and 1,958 co-citation links.The largest cluster, i.e. (#0) of the network is disconnected from the largest component of the network.In this analysis, we will consider only largest component.
The largest component of connected clusters contains 442 nodes, which is 72% of the network.The largest component is further divided into 13 smaller clusters of different sizes.Table 5illustrates the details of these clusters.
Cluster #1 (largest cluster) contains 65 nodes, which are 10.628% of whole nodes in the network.The average publication year of the literature in this cluster is 2007.The mean silhouette score of 0.737 indicates relatively high homogeneity in the cluster.
Cluster #2 contains 57 nodes, which are 9.328% of whole nodes in the network.The average publication year of the literature in this cluster is 2009.The mean silhouette score of 0.7 indicates relatively high homogeneity in the cluster.
Cluster #3 contains 56 nodes, which are 9.165% of whole nodes in the network.The average publication year of the literature in this cluster is 2008.The mean silhouette score of 0.722 indicates relatively high homogeneity in the cluster.It is interesting to note that cluster #3("AIDS") contains several articles with strongest citation burst, which indicates it is an active or emerging area of research.
Cluster #4 contains 52 nodes, which are 8.51% of whole nodes in the network.The average publication year of the literature in this cluster is 2001.The mean silhouette score of 0.791 indicates average homogeneity in the cluster.It is interesting to note that most of the highly influential articles are the members of cluster #4.
Cluster #5 contains 49 nodes, which are 8.01% of whole nodes in the network.The average publication year of the literature in this cluster is 2003.The mean silhouette score of 0.772 indicates relatively high homogeneity in the cluster.
Cluster #6 contains 45 nodes, which are 7.364% of whole nodes in the network.The average publication year of the literature in this cluster is 2012.The mean silhouette score of 0.955 indicates very high homogeneity in the cluster.
Cluster #7 contains 40 nodes, which are 6.546% of whole nodes in the network.The average publication year of the literature in this cluster is 2002.The mean silhouette score of 0.73 indicates relatively high homogeneity in the cluster.
Cluster #8 contains 19 nodes, which are 3.10% of whole nodes in the network.The average publication year of the literature in this cluster is 2003.The mean silhouette score of 0.854 indicates high homogeneity in the cluster.
Cluster #8 contains 19 nodes, which are 3.10% of whole nodes in the network.The average publication year of the literature in this cluster is 2003.The mean silhouette score of 0.854 indicates high homogeneity in the cluster.
Cluster #9 contains 18 nodes, which are 2.945% of whole nodes in the network.The average publication year of the literature in this cluster is 2004.The mean silhouette score of 0.976 indicates very high homogeneity in the cluster.
Cluster #10 contains 13 nodes, which are 2.127% of whole nodes in the network.The average publication year of the literature in this cluster is 2011.The mean silhouette score of 0.976 indicates very high homogeneity in the cluster.
Cluster #11 contains 12 nodes, which are 1.963% of whole nodes in the network.The average publication year of the literature in this cluster is 2002.The mean silhouette score of 0.944 indicates very high homogeneity in the cluster.
Cluster #12 contains 11 nodes, which are 1.800% of whole nodes in the network.The average publication year of the literature in this cluster is 1999.The mean silhouette score of 0.979 indicates very high homogeneity in the cluster.
Cluster #16 (smallest cluster) contains 5 nodes, which are 0.818% of whole nodes in the network.The average publication year of the literature in this cluster is 2010.The mean silhouette score of 0.955indicates very high homogeneity in the cluster.

Computerized Prescriber Order Entry
After an overview of the identification of clusters in the cited reference network, next, we move to the analysis of the journals.

4.2.
Analysis of Journals In this section, we visualise cited journals.Out of 1,945 records in the dataset, the 60 most cited journals were selected per one-year slice to build the network.
The pink rings around the nodes depicted in Figure 3Error!Reference source not found.indicate that there are five nodes in the network with centrality >0.1."Journal of the American Medical Informatics Association" has the largest number of highly cited publications.The second largest number of publications is associated with the "The Journal of the American Medical Association.""Proceedings of the AMIA Symposium" (2005) has the strongest citation burst among authors from the period of 2005.
Table 6gives details of the top 5 key journals based on centrality."The Journal of the American Medical Association" has the highest centrality score of 0.14 among all the other journals.It has 37.684 impact factor.In addition, it could be seen that in terms of centrality, the "Journal of the American Medical Informatics Association," the "International Journal of Medical Informatics," "The American Journal of Medicine" and the "Artificial Intelligence in Medicine" are also some of the productive journals of this domain with a centrality score of 0.13 and impact factor of 3. 428, 2.363, 5.610, and 2.142 respectively.Table 7gives details of the top 5 key journals based on their frequency of publications.It is interesting to note that the table organised in terms of frequency of publication gives a somewhat different set of key journals.The "Journal of the American Medical Informatics Association" is at the top with a frequency of 1169 publications and 3.428 impact factor.This is followed by "The Journal of the American Medical Association", "The New England Journal of Medicine," "The Archives of Internal Medicine", and the "Annals of Internal Medicine Journal"with frequencies 1961, 819, 687, and 655and impact factor 37. 684, 59.558, 17.333, and 16.593respectively.After a visual analysis of the journals, in the next section, we will analyse the authors' network.

Analysis of Co-Authors
This section analyses the author collaborative network.Figure 4displays the visualisation of the core authors of the domain.The merged network contains 346 authors and 719 co-citation links.As shown in Fig. 4, burst nodes appear as a red circle around the node.The citation burst in authors network specifies the authors who have rapidly grown the number of publications.Even though this visualisation gives a general picture of the several authors, Table 8also illustrates a comprehensive analysis of authors' network.Here we can notice that highly cited author in the network is David Bates with 59 citations.David Bates is a Prof. of Medicine at "Harvard Medical School, USA."His areas of interest are medication safety, patient safety, quality, medical informatics, and clinical decision support.Next is Adam Wright, an Assoc.Prof. of Medicine, "Harvard Medical School, USA" and"Brigham and Women's Hospital, USA".His areas of interest are health information technology, medical informatics, biomedical informatics, clinical information systems, and CDS.Dean F. Sittig is the Cristopher Sarofilm Family Prof. of Bioengineering, "Biomedical Informatics, and UTHealth, USA." CDS, electronic health records, medical informatics, and biomedical informatics are his areas of interest.Next is Blackford Middleton, an Instructor, "Harvard TH Chan School of Public Health, USA".His areas of interest include personal health record, clinical informatics, CDs, knowledge management, and electronic medical record.Finally, we have RaminKhorasani, MD, PhD, "Brigham and Women's Hospital, USA."Even though this visualisation gives a general picture of the several authors, Table 10also illustrates a comprehensive analysis of authors' network.Here we can notice that highly cited author in the network is David Bates with 460 citations.Next is Amit X. Garg, a Prof. of Medicine (Nephrology), Biostatistics & Epidemiology, "Western University, Canada".His areas of interest are kidney diseases, kidney donation, and clinical research.Following him is Kensaku Kawamoto, an Asst.Prof. of Biomedical Informatics and Assoc.CMIO in the "University of Utah, USA".Knowledge management, CDS, and standards and interoperability are his areas of interest.Next is Rainu Kaushal, "Departments of Medicine, Quality Improvement, Risk Management, and Children's Hospital, Boston, Massachusetts, USA".Finally, we have Gilad J. Kuperman, an Adjunct Assoc.Prof. of Biomedical Informatics, "Columbia University Clinical Informatics, USA".After a visual analysis of countries, we will present a visual analysis of institutions of highly cited publications.

4.6.
Analysis of Institutions In this section, visualisation of institutions is performed.Figure 8 contains a merged network of institutions of 319 nodes and 844 edges.We have selected top 50 nodes per one-year length time slice from 1,945 records.The "Harvard" is the most central, as well as highly cited node among all other institutions.Following it is the "Brigham and Women's Hospital, USA."Whereas the "University of Massachusetts, USA" has the strongest citation burst.A visual analysis of the history of the burstness of institutions identifies universities that are specifically active in the research in this domain.As shown in Figure 9, the "University of Massachusetts, USA" has the strongest and longest citation burst among all other institutes in the timespan of 2006 to 2009.The "Indiana University School of Medicine, USA" also has the longest period of the burst from 2013 till 2016.Whereas, the "Weill Cornell Graduate School of Medical Sciences, USA" has shortest citation burst.
Figure 9.History of the burstness of institutions includes names of institutions, year of publication, the strength of burstness, beginning and ending year of the citation burst.The "University of Massachusetts" has the strongest burst, whereas the "University of Massachusetts" and the "Indiana University School" have the longest period of burst among all other institutions.
Next, we performed an analysis in terms of the frequency of publications associated with the institutions.Table 12represents the top five institutions based on frequency.The "Havard, USA" has the highest ranking with the frequency of 165 publications.The "Brigham & Women's Hospital, USA" followed it closely with the frequency of 122 publications.Next is the "Vanderbilt University, USA" with the frequency of 62 publications.With 56 publications, next, we have the "University of Utah, USA".Following it, we have the "University of Washington, USA" with the frequency of 55 publications.

University of Washington USA
In the Table 13 below, we performed another analysis in terms of the centrality of the publications.Table 13contains the list of the top five universities based on the centrality.It is interesting to note that top two universities the "Harvard" and "Brigham & Women's Hospital, USA" with centrality scores 0.3 and 0.17 respectively are also the highly cited institutions.Following them is the "University of Utah, USA" with centrality score 0.14.Next is the "University of Washington, USA" with centrality score 0.09.With centrality value 0.07, it seems however that the "Heidelberg University, USA" has the lowest centrality score among all other institutions.

Heidelberg University Germany
After visualisation of institutions, in the next section, we will present an analysis of subject categories of the domain.

4.7.
Analysis of Categories In this section, our next analysis is to discover publications associated with various categories.Fig. 10 depicts the temporal visualisation of categories in the domain.This merged network contains 95 categories and 355 links (co-occurences).We have selected top 50 nodes per one-year time slice.The detailed analysis based on the centrality and frequency is given below.Table 14lists the top 5 categories based on centrality.The category "Health Care Sciences & Services" leads over other categories with centrality value 0.29.It is closely followed by "Engineering" with centrality 0.28.Next is "Computer Science" with centrality score 0.25.Following it is the "Surgery" with centrality 0.18.Subsequently, we have "Nursing" with centrality score 0.24.
For relative analysis, we have also analysed these categories in terms of frequency of publications of manuscripts.The outcomes of this analysis are illustrated underneath inTable 15.Table 15lists the top 5 categories based on frequency.With the frequency of 658, "Medical Informatics" leads the rest of the categories.Following it is the "Computer Science" with a frequency of 545.Next is "Health Care Sciences & Services" with a frequency of 495, which is followed by "Computer Science, Information Systems" and "Computer Science, Interdisciplinary Applications" with frequencies 320 and 318 respectively.After visually analysing co-authors, journals, co-cited authors, countries, institutions, and subject categories, in the end, we are presenting the summary of the results.

Summary of Results
In this paper, we have utilized CiteSpace for the analysis of various types of visualization to identify emerging trends and abrupt changes in scientific literature in the domain over time.In this section, we give an overview of the key results of the visual analysis performed in this study.Firstly, using clustering of cited references we observed Cluster #1, the "computerised decision support" is the largest cluster, which contains 65 nodes that are 10.638% of whole nodes in the network.The articles of Bates DW (1999), Stamos TD (2001), Van Der Sijs H (2008), andMiddleton B (2013) are the key turning point.The half-life of these articles is 7, 4, 5, and 3 years respectively.
Subsequent analyses verified that there is conducted diversity in authors, journals, countries, institutions, and subject categories.
In the analysis of journals, we observed that the "Journal of the American Medical Informatics Association" has the largest number of highly cited publications in the domain and "Journal of the American Medical Association" is the most central journal among all other journals.
In terms of the analysis of the author's network, we observed that Ali S. Raja (2014) has the strongest burst among top all authors of the domain since 2005.We also observed that most collaborative author in the network is David Bates, a Prof. of Medicine at the"Harvard School", has 59 citations is also the most central author with centrality score 0.33.His areas of interest are medication safety, patient safety, quality, medical informatics, and clinical decision support.It is interesting to note that David Bates is also the highly cited and most central cited author of this domain.
In the analysis of countries, top 30 countries were chosen from the entire time span of 2005-2016 for each one-year time slice.We observed that the United States has the highest frequency, which indicates the origin of key publications in the domain.Whereas Canada has the highest centrality score.Scotland has the strongest citation burst, which provides the evidence that the articles originating in the domain from Scotland have attracted a degree of attention from its research community.
On the visual analysis of institutions, we found that "The University of Massachusetts" has the strongest and longest citation burst in the timespan of 2006 to 2009.The "Indiana University School of Medicine" also has the longest period of the burst among all other institutes from 2013 till 2016.Harvard has a top ranking with a frequency of 165 publications.It is interesting to note that the Harvard is also the most central institution with the centrality score 0.3.
In the analysis of categories, we observed that the category "Health Care Sciences & Services" leads over other categories with centrality value 0.29.Whereas with a frequency of 658, the category "Medical Informatics" leads the rest of the categories.

Conclusions and Future Work
In this paper, we have demonstrated a comprehensive visual and scientometric survey of the CDSS domain.This research covers all Journal articles in Thomson Reuters from the period2005-2016.Our survey is based on real data from the Web of Science databases.This allowed us to comprehend all publications in the domain of CDSSs.
Our analysis has produced many interesting results.TheCDSS has gained the interest of the research community from the era of 2005.David Bates is the highly cited author in the literature of CDSS, whereas Ali S. Raja is the author who hasrapidly grown the number of publications during the period of study.The "Journal of the American Informatics Medical Association" is the top ranking source journal.It contributes 1169 publications during the period of study.The United States has contributed the highest number of publications, whereas the United Kingdom is the second highest productive country.Most of the contributions came from Harvard, whereas the "University of Massachusetts" remainedspecifically active in the research in this domain.The "HealthCare Sciences & Services" leads the rest of the categories inCDSS.
A significant dimension of future work is to conductscientometric analysis for identifying disease patterns,specifically in the cardiovascular, breast cancer and diabetesdomains Since the beginning of computers, physicians and other healthcare professionals have expected the time when machines would aid them in the clinical decision-making and other restorative procedures."CDSS provides clinicians, patients or individuals with knowledge and person-specific or population information, intelligently filtered or presented at appropriate times, to foster better health processes, better individual patient care, and better population health" (Osheroff and Association 2006).Ba and Wang use social network analysis in the domain of health-related online social neteorks (Ba and Wang 2013) 2.2.1.History In the late 1950s, the very first articles regarding this provision appeared and within a few years, experimental prototypes were made available (Ledley and Lusted 1959).In 1970, three advisory systems have provided a useful overview of the origin of the work on CDSS: MYCIN system by Shortliffe for the selection of antibiotic therapy (Clancey, Shortliffe et al. 1979), a system by deDombal for the diagnosis of abdominal pain (Nugent, Warner et al. 1964, Clancey, Shortliffe et al. 1979), and a system called HELP for generating inpatient medical alerts (Warner 1979, Kuperman, Gardner et al. 2013).

Figure 1 .
Figure 1.The proposed methodology (adapted from [2, 3]) for the visual analysis of clinical decision support system for the discovery of emerging patterns and trends in the bibliographic data of the domain.
Bates DW is 7 years and the half-life of the article of Thomas D. Stamos is 4 years.Whereas the half-life of Van Der Sijis H's article is 5 years and the half-life of Middleton B's paper is 3 years.

Figure 2 .
Figure 2. A merged network of cited references with 611 nodes and 1958 links on our CDSS dataset (2005-2016) based on 1year time slices.The largest component of connected clusters divided into 13 smaller clusters.The largest cluster(Niazi 2011) is "computerised decision support" and the smallest is "computerised prescriber order entry."The diameter of the circle corresponds to the frequency of the node.Whereas red circle indicates high citation burst of the article.The article of Garg AX has the highest frequency and highest citation burst among other articles of the domain.Table 3 demonstrates documents in terms of frequency.It is also interesting to note that the article by Amit X. Garg (2005) is the landmark node with the largest radii.Amit X. Garg's article also has highest citation burst of 20.71, which indicates that it has attracted huge attention from the research community.It has 223 citations 6-year half-life, whereas it has2357 citations on Google Scholar.Following it is the article of Kensaku Kawamoto (2005) with 15.46 citation burst, 151 citations, and half-life of 6 years.It has 1684 citations on Google Scholar.Next is the article by Kuperman GJ (2007) with 3.48 citation burst, 135citation frequency, and a half-life of 5 years.It has 547 citations on Google Scholar.It is closely followed by the Van DerSijs H (2007) with a citation burst of 15.09, citation frequency 116, and half-life of 5 years.It has 690 citations on Google Scholar.The article

Figure 3 .
Figure 3.Journals' network in terms of centrality.Concentric citation tree rings indicate the citation history of the publications of a journal.The colours of the circles in the tree ring represent citations in a corresponding year.The red rings indicate the citation burst of the publication.The colours of the links correspond to the time slice.The pink rings around the node indicate the centrality >= 0.1.The "J AM MED INFORM ASSN" is the highly cited journal, whereas the "Jama-j AM MED ASSOC" is the most central Journal of the domain

Figure 4 .
Figure 4. Co-authors network visualisation.The merged network contains 346 nodes and 719 links.Top 20% nodes are selected per slice (of length 3).Burst nodes appear as a red circle around the node.Concentric citation tree rings indicate the citation history of the publications of an author.David BW is the highly cited node with the frequency of 59, whereas Payne TH is the most central node with a centrality score of 0.08.Gurwitz JH and Field TS have longest citation burst periods.As shown in Figure 5, in terms of frequency, David BW is the landmark node with largest radii of the citation ring.Payne THis the most central author of this domain.Visualisation in Figure 5 illustrates the authors who have the strongest citation bursts and years in which it took place.It can be seen that Ali S. Raja(2014) from "Harvard Medical School, USA" has the strongest burst among the top 5 authors since 2005.Ivan K. Ip (2005) from "Harvard Medical School, USA" has the second strongest burst, which took place in the period of 2013 to 2016.Following him are Terry S. Field (2005) from Meyers Primary Care Institute, RaminKhorasani (2014) from "Brigham and Women's Hospital", and Jerry H. Gurwitz (2005) from "Meyers Primary Care Institute, USA."

Figure 6 .
Figure 6.Cited-authors network visualisation.The merged network contains 211 nodes and 656 links.Burst nodes appear as a red circle around the node.Concentric citation tree rings indicate the citation history of the publications of an author.The pink rings around the node indicate the centrality score >= 0.1.Bates DW is the landmark with largest radii and is also the hub node with the highest degree.

Figure 7 .
Figure 7. Countries network of 55 nodes and 263 links.The burst nodes appear as a red circle around the node.Concentric citation tree rings indicate the citation history of the publications of a country.The pink circle around the node represents the centrality >= 0.The USA is the highly cited node, whereas Canada is the most central node and Scotland has strongest citation burst.

Figure 8 .
Figure 8.The network of Institutions, containing 319 nodes and 844 edges.Concentric citation tree rings demonstrate the citation history of the publications of an institution.The purple circle represents betweenness centrality.The thicker the purple ring, the higher the centrality score.The "University of Massachusetts" has the strongest burst.The Harvard is the highly cited and most central institution of the domain.

Figure 10 .
Figure 10.The category network containing 95 categories and 355 links.Concentric citation tree rings demonstrate the citation history of the publications of an institution.The purple circle represents betweenness centrality.The thicker the purple ring, the higher the centrality score.Medical Informatics is the category with highest frequency, whereas Health Care Sciences and Services is the most central category.

Table 3 .
The summary table of cited references sorted in terms of Frequency includes frequency (F), citation burst (CB), author (AU), publication year (PY), journal (J), Volume (V), page no.(PP), half-life (HL), cluster ID (CL), and Google Scholar Citations (GSC) of the top 5 most cited references.Table 4 contains cited documents in terms of betweenness centrality.The article by Basit Chaudhry (2006) is the most influential document with the highest centrality score of 0.43.Half-life of this article is 5 years and it has 2491 citations on Google Scholar.Following it is the article by Ross Koppel (2005) with 0.24 centrality, and half-life of 5 years.It has 1995 citations on Google Scholar.Next is the article by Amit X. Garg (2005) with 0.18betweennesscentraliy and a half-life of 6 years.It has 2357 citations on Google Scholar.It is closely followed by Jerome A. Osheroff (2007) with betweenness centrality of 0.16 and half-life of 5 years.It has 357 citations on Google Scholar.Finally, we have article by Gilad J. Kuperman (2007) with lowest betweenness centrality of 0.14 among top five articles of the domain.It has a half-life of 5 years.It has 547 citations on Google Scholar.

Table 10 . The Top 5 Cited-Authors in terms of the frequency. David Bates is the most cited author with 460 citations, whereas Kuperman GJ is the least cited author with 198 citations.
For additional comparative analysis, we have observed the top-cited authors in terms of centrality.Fresh names which enter in Table11are: David Blumenthal from the "Harvard Medical School, USA" and Basit Chaudhry from the "University of California, USA."