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REFINE: A Fine-Grained Medication Recommendation System Using Deep Learning and Personalized Drug Interaction Modeling

Suman Bhoi · Mong Li Lee · Wynne Hsu · Ngiap Chuan Tan

Great Hall & Hall B1+B2 (level 1) #311
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[ Paper [ Poster [ OpenReview
Wed 13 Dec 8:45 a.m. PST — 10:45 a.m. PST


Patients with co-morbidities often require multiple medications to manage their conditions. However, existing medication recommendation systems only offer class-level medications and regard all interactions among drugs to have the same level of severity. This limits their ability to provide personalized and safe recommendations tailored to individual needs. In this work, we introduce a deep learning-based fine-grained medication recommendation system called REFINE, which is designed to improve treatment outcomes and minimize adverse drug interactions. In order to better characterize patients’ health conditions, we model the trend in medication dosage titrations and lab test responses, and adapt the vision transformer to obtain effective patient representations. We also model drug interaction severity levels as weighted graphs to learn safe drug combinations and design a balanced loss function to avoid overly conservative recommendations and miss medications that might be needed for certain conditions. Extensive experiments on two real-world datasets show that REFINE outperforms state-of-the-art techniques.

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