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Detection of Drug–Drug and Drug–Disease Interactions Inducing Acute Kidney Injury Using Deep Rule Forests

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Abstract

Drug–drug interactions (DDIs) and drug–disease interactions (DDXs) are critical issues for the healthcare system and clinical physicians. Typical statistical approaches, such as generalized linear models, cannot systematically handle the complexity of DDIs and DDXs. Although deep neural networks can predict DDIs and DDXs with high accuracy, they often require large numbers of training data, and how such black-box models arrive at predictions is still not well understood. Therefore, we propose a novel interpretable representation learning algorithm, Deep Rule Forest (DRF) to help discover rules from multi-layer tree-based models as the combinations of drug usages and disease indications to help identify DDIs and DDXs. In this paper, we introduce a real-world application of our approach to acute kidney injury (AKI), which is a typical unfavorable outcome that could be triggered by DDIs/DDXs. The sample data were obtained from a population of one million individuals randomly selected from the Taiwan National Health Insurance Database. The experimental result shows that the combinations of several diseases and drug prescriptions are associated with AKI. Also, the DRF combined with other machine learning algorithms perform comparatively higher than typical tree/rule-based and other state-of-the-art algorithms in terms of accuracy of prediction and model interpretability.

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Funding

This study was funded by the Ministry of Science and Technology, Taiwan (MOST 107-2410-H-110-031) and National Sun Yat-Sen University and Kaohsiung Medical University, Taiwan (NSYSUKMU 105-I005 and NSYSUKMU 106-I005).

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  1. Department of Information Management, National Sun Yat-Sen University, Kaohsiung, Taiwan

    Yihuang Kang & Sheng-Tai Huang

  2. Division of Nephrology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan

    Ping-Hsung Wu

  3. College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan

    Ping-Hsung Wu

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This article is part of the topical collection “Artificial Intelligence for HealthCare” guest edited by Lydia Bouzar-Benlabiod, Stuart H. Rubin, and Edwige Pissaloux.

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Kang, Y., Huang, ST. & Wu, PH. Detection of Drug–Drug and Drug–Disease Interactions Inducing Acute Kidney Injury Using Deep Rule Forests. SN COMPUT. SCI. 2, 299 (2021). https://doi.org/10.1007/s42979-021-00670-0

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