Evaluation of Graph-Based Algorithms for Early Detection of In-Hospital Mortality...36th Medical Informatics Europe (MIE) Conference, May 25-28, 2026, Genoa, Italy.
Enhancing patient care quality depends on preventing health-related adverse events (HAEs), including in-hospital mortality, right from the start of hospitalization. Today, machine learning tools offer innovative solutions for predicting such events. Nevertheless, and despite their predictive potenti...
| Publicado en: | Studies in Health Technology & Informatics Vol. 336; pp. 559 - 564 |
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| Autores principales: | , , , , |
| Formato: | proceedings research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2026
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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=194018870&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 194018870 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2026 vid: 336 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 194018870 194018870 194018870 10.3233/SHTI260233 194018870 ppf: 559 ppct: 5 formats: tig: atl: Evaluation of Graph-Based Algorithms for Early Detection of In-Hospital Mortality...36th Medical Informatics Europe (MIE) Conference, May 25-28, 2026, Genoa, Italy. aug: au: BEAUDOIN, Paul-Antoine CANCE, Christophe ACHARD, Sophie BOSSON, Jean-Luc MOREAU-GAUDRY, Alexandre affil: Univ. Grenoble Alpes, CNRS, UMR 5525, VetAgro Sup, Grenoble INP, CHU Grenoble Alpes, TIMC, 38000 Grenoble, France. sug: subj: Hospital Mortality Risk Factors Risk Assessment Machine Learning Algorithms Congresses and Conferences Italy Italy Diffusion of Innovation Data Warehouse Electronic Health Records Prediction Models Neural Networks (Computer) ROC Curve Confidence Intervals Descriptive Statistics ab: Enhancing patient care quality depends on preventing health-related adverse events (HAEs), including in-hospital mortality, right from the start of hospitalization. Today, machine learning tools offer innovative solutions for predicting such events. Nevertheless, and despite their predictive potential, graph neural networks (GNNs) remain largely unexplored in the literature for analyzing complex, interconnected, real-life clinical hospital data from a clinical data warehouse (CDW). In this study, we present a robust evaluation of two GNN models which are benchmarked against strong baseline methods for predicting in-hospital death. Our findings demonstrate that these models perform as well as the best benchmarks established in previous research. Additionally, we highlight promising opportunities enabled by the graph structure of the data, including graph model optimization and enhanced prediction explainability. pubtype: Academic Journal doctype: proceedings research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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