Medication Recommender System for ICU Patients Using Autoencoders

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.