Le carre biography sismanogleio
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Prediction of Hospitalization Using Machine Learning for Emergency Department Patients
Challenges of Trustable AI and Added-Value on Health B. Séroussi et al. (Eds.) © 2022 European Federation for Medical Informatics (EFMI) and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0). doi:10.The objective of this study was to evaluate the predictive capability of five machine learning models regarding the admission or discharge of emergency department patients. A Random Forest classifier outperformed other models with respect to the area under the receiver operating characteristic curve (AUC ROC). Keywords. machine learning, emergency department, patient admission 1. Introduction and Background One of the greatest challenges that most Emergency Departments (ED) face daily is the surge of patient volume and the limited medical resources capacity. Fast recogn
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Archivio Italiano di Urologia 3_2024
96; n. 3, September 2024
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Capturing Provenance, Evolution and Modification of Clinical Protocols via a Heterogeneous, Semantic Social Network
This work was supported by eCP: Electronic Clinical Protocols project (MIS 375876), funded under the Greek National Programme Thales, co-funded by the europeisk Commission. and FP7-ICT project CARRE (No. 611140), funded by the European Commission. Capturing Provenance, Evolution and Modification of Clinical Protocols via a Heterogeneous, Semantic Social Network Nick Portokallidis, George Drosatos and Eleni Kaldoudi School of Medicine, Democritus University of Thrace, Alexandroupoli, Greece Aim A novel approach to describe, organize, manage, trace, use and reuse clinical protocols, based on a heterogeneous semantic social network • The proposed approach allows – Semantic tagging – Semantic enrichment • Main advantages – Tracing protocol provenance, evolution and modifications – Protocol meta-description, irrespective of protocol source format – Interlinking to related