A Personalized and Learning Approach for Identifying Drugs with Adverse Events.

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.