{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,6]],"date-time":"2022-04-06T01:37:53Z","timestamp":1649209073822},"reference-count":0,"publisher":"IOS Press","license":[{"start":{"date-parts":[[2021,5,27]],"date-time":"2021-05-27T00:00:00Z","timestamp":1622073600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,5,27]]},"abstract":"<jats:p>Study of trajectory of care is attractive for predicting medical outcome. Models based on machine learning (ML) techniques have proven their efficiency for sequence prediction modeling compared to other models. Introducing pattern mining techniques contributed to reduce model complexity. In this respect, we explored methods for medical events\u2019 prediction based on the extraction of sets of relevant event sequences of a national hospital discharge database. It is illustrated to predict the risk of in-hospital mortality in acute coronary syndrome (ACS). We mined sequential patterns from the French Hospital Discharge Database. We compared several predictive models using a text string distance to measure the similarity between patients\u2019 patterns of care. We computed combinations of similarity measurements and ML models commonly used. A Support Vector Machine model coupled with edit-based distance appeared as the most effective model. Indeed discrimination ranged from 0.71 to 0.99, together with a good overall accuracy. Thus, sequential patterns mining appear motivating for event prediction in medical settings as described here for ACS.<\/jats:p>","DOI":"10.3233\/shti210167","type":"book-chapter","created":{"date-parts":[[2021,5,27]],"date-time":"2021-05-27T12:57:53Z","timestamp":1622120273000},"source":"Crossref","is-referenced-by-count":0,"title":["Prediction of In-Hospital Mortality from Administrative Data: A Sequential Pattern Mining Approach"],"prefix":"10.3233","author":[{"given":"Jessica","family":"Pinaire","sequence":"first","affiliation":[{"name":"Montpellier university UPRES EA 2415, Clinical Research University Institute"},{"name":"Montpellier University LIRMM, UMR 5506"}]},{"given":"Etienne","family":"Chabert","sequence":"additional","affiliation":[{"name":"Montpellier University LIRMM, UMR 5506"}]},{"given":"J\u00e9r\u00f4me","family":"Az\u00e9","sequence":"additional","affiliation":[{"name":"Montpellier University LIRMM, UMR 5506"}]},{"given":"Sandra","family":"Bringay","sequence":"additional","affiliation":[{"name":"Montpellier University LIRMM, UMR 5506"},{"name":"Paul Valery University, AMIS, Montpellier, France"}]},{"given":"Pascal","family":"Poncelet","sequence":"additional","affiliation":[{"name":"Montpellier University LIRMM, UMR 5506"}]},{"given":"Paul","family":"Landais","sequence":"additional","affiliation":[{"name":"Montpellier university UPRES EA 2415, Clinical Research University Institute"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Public Health and Informatics"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI210167","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,25]],"date-time":"2021-10-25T13:11:46Z","timestamp":1635167506000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI210167"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,27]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti210167","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,27]]}}}