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Explainability based on feature importance for better comprehension of machine learning in healthcare

Das PP, Wiese L, ELISE Study Group

Research article in edited proceedings (conference) | Peer reviewed

Details about the publication

Editors: Abelló A, Vassiliadis P, Romero O, Wrembel R, Bugiotti F, Gamper J, Vargas Solar G, Zumpano E
Book title: New Trends in Database and Information Systems
Page range: 324-335
Publisher: Springer
Place of publication: Cham
Status: Published
Release year: 2023
Conference: EXEC-MAN (EXplainable hEalthCare data Management and ANalytics), Barcelona, Spain
DOI: 10.1007/978-3-031-42941-5_28
Link to the full text: https://link.springer.com/chapter/10.1007/978-3-031-42941-5_28
Keywords: Explainability; AI

Authors from the University of Münster

Böhnke, Julia
Institute of Epidemiology and Social Medicine
Karch, André
Institute of Epidemiology and Social Medicine
Rübsamen, Nicole
Institute of Epidemiology and Social Medicine

Projects the publication originates from

Ein Lernendes und Interoperables, Smartes Expertensystem für die pädiatrische Intensivmedizin (ELISE)
Duration: 01/10/2020 - 30/09/2023
Funded by: Bundesministerium für Gesundheit
Type of project: Participation in federally funded joint project