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Interpretable Feature Extraction from Clinical Notes for Sepsis Prediction: Comparing Rule-Based, LLM, and Hybrid Approaches.

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Bibliographic Details
Title: Interpretable Feature Extraction from Clinical Notes for Sepsis Prediction: Comparing Rule-Based, LLM, and Hybrid Approaches.
Authors: Frey N; Institute of Medical Informatics, Charité - Universitätsmedizin Berlin, Germany., Meyer-Eschenbach F; Institute of Medical Informatics, Charité - Universitätsmedizin Berlin, Germany.; Clinical Study Center, Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Germany., Voge L; Institute of Medical Informatics, Charité - Universitätsmedizin Berlin, Germany., Ruhm L; Clinical Study Center, Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Germany., Wagnitz J; Institute of Medical Informatics, Charité - Universitätsmedizin Berlin, Germany., Näher AF; Institute of Medical Informatics, Charité - Universitätsmedizin Berlin, Germany., Grünewald E; Institute of Medical Informatics, Charité - Universitätsmedizin Berlin, Germany., Nothacker J; Clinical Study Center, Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Germany., von Kalle C; Clinical Study Center, Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Germany., Kumpf O; Institute of Medical Informatics, Charité - Universitätsmedizin Berlin, Germany., Balzer F; Institute of Medical Informatics, Charité - Universitätsmedizin Berlin, Germany.
Source: Studies in health technology and informatics [Stud Health Technol Inform] 2026 May 21; Vol. 336, pp. 989-993.
Publication Type: Journal Article; Comparative Study
Journal Info: Publisher: IOS Press Country of Publication: Netherlands NLM ID: 9214582 Publication Model: Print Cited Medium: Internet ISSN: 1879-8365 (Electronic) Linking ISSN: 09269630 NLM ISO Abbreviation: Stud Health Technol Inform Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1879-8365
DOI:10.3233/SHTI260327