Improving Cardiology-Rehospitalization Prediction Through the Synergy of Process Mining and Deep Learning: An Innovative Approach...European Federation for Medical Informatics (EFMI) Special Topic Conference, October 25-27, 2023, Turin, Italy.
Nowadays, hospitals are facing the need for an accurate prediction of rehospitalizations. Rehospitalizations, indeed, represent both a high financial burden for the hospital and a proxy measure of care quality. The current work aims to address such a problem with an innovative approach, by building...
| Publicado en: | Studies in Health Technology & Informatics Vol. 309; pp. 238 - 240 |
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| Autores principales: | , , , |
| Formato: | proceedings research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2023
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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=173212696&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 173212696 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2023 vid: 309 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 173212696 173212696 173212696 10.3233/SHTI230786 173212696 ppf: 238 ppct: 2 formats: tig: atl: Improving Cardiology-Rehospitalization Prediction Through the Synergy of Process Mining and Deep Learning: An Innovative Approach...European Federation for Medical Informatics (EFMI) Special Topic Conference, October 25-27, 2023, Turin, Italy. aug: au: SPIZZI, Eleonora QUADRARO, Damiano ESPOSTI, Federico FERRARIO, Manuela affil: Politecnico di Milano, Italy sug: subj: Readmission Evaluation Data Mining Deep Learning Congresses and Conferences Italy Italy Human Quality of Health Care Hospitals Economics Hospitalization Patient Record Systems Risk Assessment Health and Welfare Planning Cause of Death Health Facility Departments Algorithms ROC Curve Logistic Regression Random Forest Treatment Outcomes ab: Nowadays, hospitals are facing the need for an accurate prediction of rehospitalizations. Rehospitalizations, indeed, represent both a high financial burden for the hospital and a proxy measure of care quality. The current work aims to address such a problem with an innovative approach, by building a Process Mining-Deep Learning model for the prediction of 6-months rehospitalization of patients hospitalized in a Cardiology specialty at San Raffaele Hospital, starting from their medical history contained in the Patients Hospital Records, with the double purpose of supporting resource planning and identifying at-risk patients. pubtype: Academic Journal doctype: proceedings research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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