Prediction of In-Hospital Mortality from Administrative Data: A Sequential Pattern Mining Approach...Medical Informatics Europe, Public Health and Informatics Conference (Virtual), 29-31 May, 2021.
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 res...
| Publicado en: | Studies in Health Technology & Informatics no. 281; pp. 293 - 298 |
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| Autores principales: | , , , , , |
| Formato: | proceedings research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=150593555&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 150593555 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2021 iid: 281 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 150593555 150593555 150593555 10.3233/SHTI210167 150593555 ppf: 293 ppct: 5 formats: tig: atl: Prediction of In-Hospital Mortality from Administrative Data: A Sequential Pattern Mining Approach...Medical Informatics Europe, Public Health and Informatics Conference (Virtual), 29-31 May, 2021. aug: au: PINAIRE, Jessica CHABERT, Etienne AZÉ, Jérôme BRINGAY, Sandra PONCELET, Pascal LANDAIS, Paul affil: Montpellier university UPRES EA 2415, Clinical Research University Institute sug: subj: Acute Coronary Syndrome Mortality Hospital Mortality Risk Assessment Teleconferencing Human Data Mining Support Vector Machine ab: 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' 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' 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. pubtype: Academic Journal doctype: proceedings research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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