{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T19:33:33Z","timestamp":1761766413555,"version":"3.41.2"},"reference-count":48,"publisher":"Emerald","issue":"1","license":[{"start":{"date-parts":[[2019,2,4]],"date-time":"2019-02-04T00:00:00Z","timestamp":1549238400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IMDS"],"published-print":{"date-parts":[[2019,2,4]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>The purpose of this paper is to formulate a framework to construct a patient-specific risk score and therefore to classify these patients into various risk groups that can be used as a decision support mechanism by the medical decision makers to augment their decision-making process, allowing them to optimally use the limited resources available.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>A conventional statistical model (logistic regression) and two machine learning-based (i.e. artificial neural networks (ANNs) and support vector machines) data mining models were employed by also using five-fold cross-validation in the classification phase. In order to overcome the data imbalance problem, random undersampling technique was utilized. After constructing the patient-specific risk score, k-means clustering algorithm was employed to group these patients into risk groups.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>Results showed that the ANN model achieved the best results with an area under the curve score of 0.867, while the sensitivity and specificity were 0.715 and 0.892, respectively. Also, the construction of patient-specific risk scores offer useful insights to the medical experts, by helping them find a trade-off between risks, costs and resources.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>The study contributes to the existing body of knowledge by constructing a framework that can be utilized to determine the risk level of the targeted patient, by employing data mining-based predictive approach.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/imds-12-2017-0579","type":"journal-article","created":{"date-parts":[[2018,8,23]],"date-time":"2018-08-23T08:16:59Z","timestamp":1535012219000},"page":"189-209","source":"Crossref","is-referenced-by-count":22,"title":["A comparative data analytic approach to construct a risk trade-off for cardiac patients\u2019 re-admissions"],"prefix":"10.1108","volume":"119","author":[{"given":"Murtaza","family":"Nasir","sequence":"first","affiliation":[]},{"given":"Carole","family":"South-Winter","sequence":"additional","affiliation":[]},{"given":"Srini","family":"Ragothaman","sequence":"additional","affiliation":[]},{"given":"Ali","family":"Dag","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"issue":"1","key":"key2020092502595275200_ref001","first-page":"39","article-title":"The economics of health care quality and medical errors","volume":"39","year":"2012","journal-title":"Journal of Health Care Finance"},{"volume-title":"Heaviside\u2019s Operational Calculus as Applied to Engineering and Physics: As Applied to Engineering and Physics","year":"1936","key":"key2020092502595275200_ref002"},{"issue":"1","key":"key2020092502595275200_ref003","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1300\/J010v16n01_03","article-title":"Predicting elderly cardiac patients at risk for readmission","volume":"16","year":"1992","journal-title":"Social Work in Health Care"},{"volume-title":"Data Mining Techniques: For Marketing, Sales, and Customer Support","year":"1997","key":"key2020092502595275200_ref004"},{"key":"key2020092502595275200_ref005","doi-asserted-by":"crossref","unstructured":"Chawla, N.V. (2005), \u201cData mining for imbalanced datasets: an overview\u201d, in Maimon, O. and Rokach, L. (Eds), Data Mining and Knowledge Discovery Handbook, Springer, Boston, MA, pp. 853-867.","DOI":"10.1007\/0-387-25465-X_40"},{"key":"key2020092502595275200_ref007","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.dss.2016.02.007","article-title":"A probabilistic data-driven framework for scoring the preoperative recipient-donor heart transplant survival","volume":"86","year":"2016","journal-title":"Decision Support Systems"},{"key":"key2020092502595275200_ref006","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.dss.2016.10.005","article-title":"Predicting heart transplantation outcomes through data analytics","volume":"94","year":"2017","journal-title":"Decision Support Systems"},{"issue":"1","key":"key2020092502595275200_ref008","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1111\/j.1468-0394.2008.00480.x","article-title":"Analysis of cancer data: a data mining approach","volume":"26","year":"2009","journal-title":"Expert Systems"},{"issue":"3","key":"key2020092502595275200_ref009","doi-asserted-by":"crossref","first-page":"698","DOI":"10.1016\/j.dss.2011.11.004","article-title":"An analytic approach to better understanding and management of coronary surgeries","volume":"52","year":"2012","journal-title":"Decision Support Systems"},{"volume-title":"The Elements of Statistical Learning (Vol. 1): Springer Series in Statistics","year":"2001","key":"key2020092502595275200_ref010"},{"key":"key2020092502595275200_ref011","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.jbi.2015.05.016","article-title":"A comparison of models for predicting early hospital readmissions","volume":"56","year":"2015","journal-title":"Journal of Biomedical Informatics"},{"key":"key2020092502595275200_ref012","first-page":"85","article-title":"Support vector machines for classification and regression","volume":"14","year":"1998","journal-title":"ISIS Technical Report"},{"key":"key2020092502595275200_ref013","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1109\/ICNC.2008.871","article-title":"On the class imbalance problem","year":"2008"},{"volume-title":"Data Mining: Concepts and Techniques","year":"2011","key":"key2020092502595275200_ref014"},{"issue":"6","key":"key2020092502595275200_ref015","doi-asserted-by":"publisher","first-page":"773","DOI":"10.1001\/jama.290.6.773","article-title":"Predictors of readmission for complications of coronary artery bypass graft surgery","volume":"290","year":"2003","journal-title":"Jama"},{"issue":"1","key":"key2020092502595275200_ref016","first-page":"100","article-title":"Algorithm AS 136: A k-means clustering algorithm","volume":"28","year":"1979","journal-title":"Journal of the Royal Statistical Society: Series C (Applied Statistics)"},{"issue":"9","key":"key2020092502595275200_ref017","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","article-title":"Learning from imbalanced data","volume":"21","year":"2009","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"5","key":"key2020092502595275200_ref018","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1136\/hrt.77.5.437","article-title":"Predictors of hospital readmission two years after coronary artery bypass grafting","volume":"77","year":"1997","journal-title":"Heart"},{"issue":"5","key":"key2020092502595275200_ref019","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1016\/0893-6080(90)90005-6","article-title":"Universal approximation of an unknown mapping and its derivatives","volume":"3","year":"1990","journal-title":"Neural Networks"},{"issue":"16","key":"key2020092502595275200_ref020","doi-asserted-by":"crossref","first-page":"1681","DOI":"10.1001\/jama.2015.13254","article-title":"Seeking rational approaches to fixing hospital readmissions","volume":"314","year":"2015","journal-title":"Jama"},{"first-page":"121","article-title":"Irrelevant features and the subset selection problem","year":"1994","key":"key2020092502595275200_ref021"},{"issue":"2","key":"key2020092502595275200_ref022","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1007\/s10796-016-9641-2","article-title":"Big data and analytics in healthcare: Introduction to the special section","volume":"18","year":"2016","journal-title":"Information Systems Frontiers"},{"volume-title":"Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models","year":"2001","key":"key2020092502595275200_ref023"},{"issue":"2","key":"key2020092502595275200_ref024","first-page":"1137","article-title":"A study of cross-validation and bootstrap for accuracy estimation and model selection","volume":"14","year":"1995","journal-title":"IJCAI"},{"issue":"6","key":"key2020092502595275200_ref025","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1093\/bjaceaccp\/mkn041","article-title":"Clinical tests: sensitivity and specificity","volume":"8","year":"2008","journal-title":"Continuing Education in Anaesthesia, Critical Care & Pain"},{"first-page":"245","article-title":"Selection of relevant features in machine learning","year":"1994","key":"key2020092502595275200_ref026"},{"issue":"1","key":"key2020092502595275200_ref027","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1108\/IMDS-12-2015-0509","article-title":"Predicting customer churn in mobile industry using data mining technology","volume":"117","year":"2017","journal-title":"Industrial Management & Data Systems"},{"issue":"5","key":"key2020092502595275200_ref028","doi-asserted-by":"publisher","first-page":"729","DOI":"10.1161\/circoutcomes.112.966945","article-title":"Hospital variation in readmission after coronary artery bypass surgery in California","volume":"5","year":"2012","journal-title":"Circulation Cardiovascular Quality and Outcomes"},{"issue":"12","key":"key2020092502595275200_ref029","doi-asserted-by":"crossref","first-page":"11303","DOI":"10.1016\/j.eswa.2012.02.063","article-title":"Data mining techniques and applications \u2013 a decade review from 2000 to 2011","volume":"39","year":"2012","journal-title":"Expert Systems with Applications"},{"issue":"1","key":"key2020092502595275200_ref030","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.ijcard.2005.07.015","article-title":"Predicting readmissions and cardiovascular events in heart failure patients","volume":"109","year":"2006","journal-title":"International Journal of Cardiology"},{"volume-title":"Support Vector Machines: Training and Applications","year":"1997","key":"key2020092502595275200_ref031"},{"issue":"8","key":"key2020092502595275200_ref032","doi-asserted-by":"crossref","first-page":"1678","DOI":"10.1108\/IMDS-09-2015-0363","article-title":"A hybrid data analytic approach to predict college graduation status and its determinative factors","volume":"116","year":"2016","journal-title":"Industrial Management & Data Systems"},{"volume-title":"Artificial Neural Networks: Theory and Applications","year":"1998","key":"key2020092502595275200_ref033"},{"first-page":"217","article-title":"Reducing misclassification costs","year":"1994","key":"key2020092502595275200_ref034"},{"issue":"2","key":"key2020092502595275200_ref035","first-page":"224","article-title":"Addressing the class imbalance problem in medical datasets","volume":"3","year":"2013","journal-title":"International Journal of Machine Learning and Computing"},{"issue":"19","key":"key2020092502595275200_ref036","doi-asserted-by":"crossref","first-page":"2507","DOI":"10.1093\/bioinformatics\/btm344","article-title":"A review of feature selection techniques in bioinformatics","volume":"23","year":"2007","journal-title":"Bioinformatics"},{"issue":"2","key":"key2020092502595275200_ref037","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.eswa.2005.07.018","article-title":"Predicting box-office success of motion pictures with neural networks","volume":"30","year":"2006","journal-title":"Expert Systems with Applications"},{"volume-title":"Introduction to Data Mining and Its Applications","year":"2006","key":"key2020092502595275200_ref038"},{"key":"key2020092502595275200_ref039","first-page":"97","article-title":"Predicting graft survival among kidney transplant recipients: a Bayesian decision support model","volume":"106","year":"2017","journal-title":"Decision Support Systems"},{"issue":"12","key":"key2020092502595275200_ref040","doi-asserted-by":"crossref","first-page":"1134","DOI":"10.1056\/NEJMsa1303118","article-title":"Variation in surgical-readmission rates and quality of hospital care","volume":"369","year":"2013","journal-title":"New England Journal of Medicine"},{"issue":"88","key":"key2020092502595275200_ref041","first-page":"1","article-title":"Determinants of preventable readmissions in the United States: a systematic review","volume":"5","year":"2010","journal-title":"Implementation Science"},{"issue":"2","key":"key2020092502595275200_ref042","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.ijcard.2011.06.119","article-title":"Prediction of 30-day cardiac-related-emergency-readmissions using simple administrative hospital data","volume":"164","year":"2013","journal-title":"International Journal of Cardiology"},{"issue":"7","key":"key2020092502595275200_ref043","doi-asserted-by":"crossref","first-page":"1426","DOI":"10.1108\/IMDS-08-2016-0342","article-title":"Development of an intelligent e-healthcare system for the domestic care industry","volume":"117","year":"2017","journal-title":"Industrial Management & Data Systems"},{"issue":"20","key":"key2020092502595275200_ref044","doi-asserted-by":"crossref","first-page":"7110","DOI":"10.1016\/j.eswa.2015.04.066","article-title":"Predictive modeling of hospital readmissions using metaheuristics and data mining","volume":"42","year":"2015","journal-title":"Expert Systems with Applications"},{"issue":"7","key":"key2020092502595275200_ref045","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1097\/00005650-199907000-00002","article-title":"Prediction of readmissions after CABG using detailed follow-up data: the Israeli CABG Study (ISCAB)","volume":"37","year":"1999","journal-title":"Medical Care"},{"issue":"14","key":"key2020092502595275200_ref046","doi-asserted-by":"crossref","first-page":"1418","DOI":"10.1056\/NEJMsa0803563","article-title":"Rehospitalizations among patients in the medicare fee-for-service program","volume":"360","year":"2009","journal-title":"New England Journal of Medicine"},{"key":"key2020092502595275200_ref047","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1146\/annurev-med-022613-090415","article-title":"Reducing hospital readmission rates: current strategies and future directions","volume":"65","year":"2014","journal-title":"Annual Review of Medicine"},{"key":"key2020092502595275200_ref048","unstructured":"Sanford Health. available at: https:\/\/en.wikipedia.org\/w\/index.php?title=Sanford_Health&oldid=789160676 (accessed July 17, 2017)."}],"container-title":["Industrial Management &amp; Data Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IMDS-12-2017-0579\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IMDS-12-2017-0579\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T21:54:18Z","timestamp":1753394058000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/imds\/article\/119\/1\/189-209\/185322"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,2,4]]},"references-count":48,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019,2,4]]}},"alternative-id":["10.1108\/IMDS-12-2017-0579"],"URL":"https:\/\/doi.org\/10.1108\/imds-12-2017-0579","relation":{},"ISSN":["0263-5577"],"issn-type":[{"type":"print","value":"0263-5577"}],"subject":[],"published":{"date-parts":[[2019,2,4]]}}}