{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T16:40:11Z","timestamp":1760892011661,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031822247"},{"type":"electronic","value":"9783031822254"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T00:00:00Z","timestamp":1743120000000},"content-version":"vor","delay-in-days":86,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Many hospitals in the world are under pressure to improve their efficiency and effectiveness so that they can achieve better health outcomes with limited resources. One common measure of performance is the rate of\u00a0unplanned hospital readmissions (UHRs) within 30-days. Emergency readmissions for the same disease can be assumed to indicate inappropriate discharge or poor planning, are costly, increase patients\u2019 mortality risks and put additional pressure on bed capacity. Data Mining (DM) techniques have been used to predict UHRs based on clinical and demographic features, but these ignore the process perspective. Predictive Process Monitoring (PPM) is a process mining technique using completed traces to make predictions for in progress cases with machine learning (ML) algorithms. The Outcome-Oriented PPM (OOPPM) is a sub-technique of PPM focusing on predicting categorical outcomes of process. Adaptation of OOPPM in healthcare settings has been limited to date. Here, we illustrate how to implement OOPPM in a healthcare context through an application of an OOPPM pipeline to hospital admissions using the open access MIMIC-IV dataset. Clinical, demographical and process features were used to build an extended event log, which was then employed for UHRs prediction. Results show prediction using OOPPM techniques outperformed traditional DM techniques. OOPPM tests using tree-based ML algorithms achieved better results compared to OOPPM tests using other ML algorithms. Our results suggest OOPPM can make a significant contribution to better understanding of hospital performance.<\/jats:p>","DOI":"10.1007\/978-3-031-82225-4_31","type":"book-chapter","created":{"date-parts":[[2025,3,30]],"date-time":"2025-03-30T03:05:58Z","timestamp":1743303958000},"page":"421-433","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Predicting Unplanned Hospital Readmissions Using Outcome-Oriented Predictive Process Mining"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4746-3446","authenticated-orcid":false,"given":"Abdulaziz","family":"Aljebreen","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2930-6077","authenticated-orcid":false,"given":"Allan","family":"Pang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7162-4425","authenticated-orcid":false,"given":"Marc","family":"de Kamps","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3998-541X","authenticated-orcid":false,"given":"Owen","family":"Johnson","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,28]]},"reference":[{"issue":"14","key":"31_CR1","doi-asserted-by":"publisher","first-page":"1418","DOI":"10.1056\/NEJMsa0803563","volume":"360","author":"S Jencks","year":"2009","unstructured":"Jencks, S., Williams, M., Coleman, E.: Rehospitalizations among patients in the Medicare fee-for-service program. N. Engl. J. Med. 360(14), 1418\u20131428 (2009)","journal-title":"N. Engl. J. Med."},{"key":"31_CR2","doi-asserted-by":"publisher","first-page":"930","DOI":"10.1007\/s11606-010-1370-5","volume":"25","author":"A Kalra","year":"2010","unstructured":"Kalra, A., Fisher, R., Axelrod, P.: Decreased length of stay and cumulative hospitalized days despite increased patient admissions and readmissions in an area of urban poverty. J. Gen. Intern. Med. 25, 930\u2013935 (2010)","journal-title":"J. Gen. Intern. Med."},{"key":"31_CR3","doi-asserted-by":"crossref","unstructured":"Glans, M., Kragh, A., Jakobsson, U.: Risk factors for hospital readmission in older adults within 30 days of discharge\u2013a comparative retrospective study. BMC Geriatrics 20 (2020)","DOI":"10.1186\/s12877-020-01867-3"},{"key":"31_CR4","doi-asserted-by":"crossref","unstructured":"Assaf, R., Jayousi, R.: 30-day hospital readmission prediction using MIMIC data. In: 14th International Conference on Application of Information and Communication Technologies (AICT), pp. 1\u20136. IEEE (2020)","DOI":"10.1109\/AICT50176.2020.9368625"},{"key":"31_CR5","doi-asserted-by":"crossref","unstructured":"Tey, F., Liu, C., Chien, T.: Predicting the 14-day hospital readmission of patients with pneumonia using artificial neural networks (ANN). Int. J. Environ. Res. Public Health 18(10) (2021)","DOI":"10.3390\/ijerph18105110"},{"key":"31_CR6","doi-asserted-by":"crossref","unstructured":"Thapa, N., Seifollahi, S., Taheri, S.: Hospital readmission prediction using clinical admission notes. In: 2022 Australasian Computer Science Week Proceedings, pp. 193\u2013199 (2022)","DOI":"10.1145\/3511616.3513115"},{"issue":"2","key":"31_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3301300","volume":"13","author":"I Teinemaa","year":"2019","unstructured":"Teinemaa, I., Dumas, M., Rosa, M.: Outcome-oriented predictive process monitoring: review and benchmark. ACM Trans. Knowl. Discov. Data 13(2), 1\u201357 (2019)","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"31_CR8","unstructured":"Munoz-Gama, J., Martin, N., Fernandez-Llatas, C.: Process mining for healthcare: characteristics and challenges. J. Biomed. Inform. 1(127), (2022)"},{"key":"31_CR9","doi-asserted-by":"crossref","unstructured":"Cremerius, J., K\u00f6nig, M., Warmuth, C.: Patient discharge classification based on the hospital treatment process. In: International Conference on Process Mining, pp. 314\u2013326. 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