Work

Medication Recommender System for ICU Patients Using Autoencoders

Year: 2025

Type: book-chapter

Abstract: Patients admitted to the intensive care unit (ICU) are often treated with multiple high-risk medications. Over- and underprescribing of indicated medications, and inappropriate choice of medications frequently occur in the ICU. This risk has to be minimized. We evaluate the performance of recommendation methods in suggesting appropriate medications and examine whether incorporating clinical patient data beyond the medication list improves recommendations. Using the MIMIC-III dataset, we formulate medication list completion as a recommendation task. Our analysis includes four autoencoder-based approaches and two strong baselines. We used as inputs either only known medications, or medications together with patient data. We showed that medication recommender systems based on autoencoders may successfully recommend medications in the ICU.

Source: Studies in health technology and informatics

DOI: 10.3233/shti250621

Authors: Tsvetan R. Yordanov, Ameen Abu-Hanna, Anita C.J. Ravelli, Iacopo Vagliano

Institutions: Amsterdam Public Health, Amsterdam University Medical Centers, University of Amsterdam

Language: English

Topic: Machine Learning in Healthcare

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

Funder: Nederlandse Organisatie voor Wetenschappelijk Onderzoek