Drug Recommendation from Diagnosis Codes: Classification vs. Collaborative Filtering Approaches

Apichat Sae-Ang, Sawrawit Chairat, Natchada Tansuebchueasai, Orapan Fumaneeshoat, Thammasin Ingviya and Sitthichok Chaichulee ()
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Apichat Sae-Ang: College of Digital Science, Prince of Songkla University, Songkhla 90110, Thailand
Sawrawit Chairat: Department of Biomedical Sciences and Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Songkhla 90110, Thailand
Natchada Tansuebchueasai: Department of Ophthalmology, Faculty of Medicine, Prince of Songkla University, Songkhla 90110, Thailand
Orapan Fumaneeshoat: Department of Family and Preventive Medicine, Faculty of Medicine, Prince of Songkla University, Songkhla 90110, Thailand
Thammasin Ingviya: Department of Family and Preventive Medicine, Faculty of Medicine, Prince of Songkla University, Songkhla 90110, Thailand
Sitthichok Chaichulee: Department of Biomedical Sciences and Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Songkhla 90110, Thailand

IJERPH, 2022, vol. 20, issue 1, 1-17

Abstract: Over time, large amounts of clinical data have accumulated in electronic health records (EHRs), making it difficult for healthcare professionals to navigate and make patient-centered decisions. This underscores the need for healthcare recommendation systems that help medical professionals make faster and more accurate decisions. This study addresses drug recommendation systems that generate an appropriate list of drugs that match patients’ diagnoses. Currently, recommendations are manually prepared by physicians, but this is difficult for patients with multiple comorbidities. We explored approaches to drug recommendations based on elderly patients with diabetes, hypertension, and cardiovascular disease who visited primary-care clinics and often had multiple conditions. We examined both collaborative filtering approaches and traditional machine-learning classifiers. The hybrid model between the two yielded a recall at 5 of 76.61%, a precision at 5 of 46.20%, a macro-averaged area under the curve of 74.52%, and an average physician agreement of 47.50%. Although collaborative filtering is widely used in recommendation systems, our results showed that it consistently underperformed traditional classification. Collaborative filtering was sensitive to class imbalances and favored the more popular classes. This study highlighted challenges that need to be addressed when developing recommendation systems in EHRs.

Keywords: machine learning; collaborative filtering; classificaiton; diseases; electronic medical prescriptions; recommender systems (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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