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Machine learning models for predicting severe COVID-19 outcomes in hospitals.
Wendland, Philipp; Schmitt, Vanessa; Zimmermann, Jörg; Häger, Lukas; Göpel, Siri; Schenkel-Häger, Christof; Kschischo, Maik.
Afiliación
  • Wendland P; University of Applied Sciences Koblenz, Department of Mathematics and Technology, Remagen, DE, Germany.
  • Schmitt V; University of Applied Sciences Koblenz, Department of Mathematics and Technology, Remagen, DE, Germany.
  • Zimmermann J; University of Applied Sciences Koblenz, Department of Mathematics and Technology, Remagen, DE, Germany.
  • Häger L; University Clinic Tübingen, Department of Internal Medicine 1, Tübingen, DE, Germany.
  • Göpel S; University Clinic Tübingen, Department of Internal Medicine 1, Tübingen, DE, Germany.
  • Schenkel-Häger C; University of Applied Sciences Koblenz, Department of Economics and Social Care, Remagen, DE, Germany.
  • Kschischo M; University of Applied Sciences Koblenz, Department of Mathematics and Technology, Remagen, DE, Germany.
Inform Med Unlocked ; 37: 101188, 2023.
Article en En | MEDLINE | ID: mdl-36742350
The aim of this observational retrospective study is to improve early risk stratification of hospitalized Covid-19 patients by predicting in-hospital mortality, transfer to intensive care unit (ICU) and mechanical ventilation from electronic health record data of the first 24 h after admission. Our machine learning model predicts in-hospital mortality (AUC = 0.918), transfer to ICU (AUC = 0.821) and the need for mechanical ventilation (AUC = 0.654) from a few laboratory data of the first 24 h after admission. Models based on dichotomous features indicating whether a laboratory value exceeds or falls below a threshold perform nearly as good as models based on numerical features. We devise completely data-driven and interpretable machine-learning models for the prediction of in-hospital mortality, transfer to ICU and mechanical ventilation for hospitalized Covid-19 patients within 24 h after admission. Numerical values of. CRP and blood sugar and dichotomous indicators for increased partial thromboplastin time (PTT) and glutamic oxaloacetic transaminase (GOT) are amongst the best predictors.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Inform Med Unlocked Año: 2023 Tipo del documento: Article País de afiliación: Alemania

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Inform Med Unlocked Año: 2023 Tipo del documento: Article País de afiliación: Alemania