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Volume 20
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10.3390/ijerph20010309
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Article

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

by
Apichat Sae-Ang
1,
Sawrawit Chairat
2,
Natchada Tansuebchueasai
3,
Orapan Fumaneeshoat
4,5,
Thammasin Ingviya
4,5 and
Sitthichok Chaichulee
2,5,*
1
College of Digital Science, Prince of Songkla University, Songkhla 90110, Thailand
2
Department of Biomedical Sciences and Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Songkhla 90110, Thailand
3
Department of Ophthalmology, Faculty of Medicine, Prince of Songkla University, Songkhla 90110, Thailand
4
Department of Family and Preventive Medicine, Faculty of Medicine, Prince of Songkla University, Songkhla 90110, Thailand
5
Research Center for Medical Data Analytics, Faculty of Medicine, Prince of Songkla University, Songkhla 90110, Thailand
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2023, 20(1), 309; https://doi.org/10.3390/ijerph20010309
Submission received: 24 November 2022 / Revised: 18 December 2022 / Accepted: 20 December 2022 / Published: 25 December 2022
(This article belongs to the Special Issue Application of Information Technology in Medicine and Healthcare)
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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 machine learning; collaborative filtering; classificaiton; diseases; electronic medical prescriptions; recommender systems

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MDPI and ACS Style

Sae-Ang, A.; Chairat, S.; Tansuebchueasai, N.; Fumaneeshoat, O.; Ingviya, T.; Chaichulee, S. Drug Recommendation from Diagnosis Codes: Classification vs. Collaborative Filtering Approaches. Int. J. Environ. Res. Public Health 2023, 20, 309. https://doi.org/10.3390/ijerph20010309

AMA Style

Sae-Ang A, Chairat S, Tansuebchueasai N, Fumaneeshoat O, Ingviya T, Chaichulee S. Drug Recommendation from Diagnosis Codes: Classification vs. Collaborative Filtering Approaches. International Journal of Environmental Research and Public Health. 2023; 20(1):309. https://doi.org/10.3390/ijerph20010309

Chicago/Turabian Style

Sae-Ang, Apichat, Sawrawit Chairat, Natchada Tansuebchueasai, Orapan Fumaneeshoat, Thammasin Ingviya, and Sitthichok Chaichulee. 2023. "Drug Recommendation from Diagnosis Codes: Classification vs. Collaborative Filtering Approaches" International Journal of Environmental Research and Public Health 20, no. 1: 309. https://doi.org/10.3390/ijerph20010309

APA Style

Sae-Ang, A., Chairat, S., Tansuebchueasai, N., Fumaneeshoat, O., Ingviya, T., & Chaichulee, S. (2023). Drug Recommendation from Diagnosis Codes: Classification vs. Collaborative Filtering Approaches. International Journal of Environmental Research and Public Health, 20(1), 309. https://doi.org/10.3390/ijerph20010309

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