Yonsei Med J.  2017 Nov;58(6):1229-1236. 10.3349/ymj.2017.58.6.1229.

A Personalized and Learning Approach for Identifying Drugs with Adverse Events

• Shin SK
• Hur H
• Cheon EK
• Oh OH
• Lee JS
• Ko WJ
• Kim BS
• Kwon Y

• KMID: 2418907
• DOI: http://doi.org/10.3349/ymj.2017.58.6.1229

Abstract

PURPOSE
Adverse drug events (ADEs) are associated with high health and financial costs and have increased as more elderly patients treated with multiple medications emerge in an aging society. It has thus become challenging for physicians to identify drugs causing adverse events. This study proposes a novel approach that can improve clinical decision making with recommendations on ADE causative drugs based on patient information, drug information, and previous ADE cases.
MATERIALS AND METHODS
We introduce a personalized and learning approach for detecting drugs with a specific adverse event, where recommendations tailored to each patient are generated using data mining techniques. Recommendations could be improved by learning the associations of patients and ADEs as more ADE cases are accumulated through iterations. After consulting the system-generated recommendations, a physician can alter prescriptions accordingly and report feedback, enabling the system to evolve with actual causal relationships.
RESULTS
A prototype system is developed using ADE cases reported over 1.5 years and recommendations obtained from decision tree analysis are validated by physicians. Two representative cases demonstrate that the personalized recommendations could contribute to more prompt and accurate responses to ADEs.
CONCLUSION
The current system where the information of individual drugs exists but is not organized in such a way that facilitates the extraction of relevant information together can be complemented with the proposed approach to enhance the treatment of patients with ADEs. Our illustrative results show the promise of the proposed system and further studies are expected to validate its performance with quantitative measures.

Keyword

Adverse drug event; data mining; clinical decision making; learning

MeSH Terms

Adult
*Adverse Drug Reaction Reporting Systems
Aged
Aged, 80 and over
*Data Mining
*Decision Support Techniques
Drug-Related Side Effects and Adverse Reactions/*prevention & control
Female
Humans
Male
Middle Aged
Physicians

Figure

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  Fig. 1 Overview of the proposed approach. ADEs, adverse drug events.

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  Fig. 2 Overall flow of the proposed system.

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  Fig. 3 Overall flow of the proposed system for case 1 (Steps 1–4).

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  Fig. 4 Decision tree and rules generated from RapidMiner (Case 1).

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  Fig. 5 Explanations on how recommendations are generated (Case 2).

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