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Zhang L, Yu HM, Li JY, Huang L, Cheng SQ, Xiao J. Data - Knowledge driven machine learning model for cancer pain medication decisions. Int J Med Inform 2025; 195:105727. [PMID: 39642589 DOI: 10.1016/j.ijmedinf.2024.105727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2024] [Revised: 10/29/2024] [Accepted: 11/27/2024] [Indexed: 12/09/2024]
Abstract
BACKGROUND Cancer pain is one of the most common symptoms in cancer patients, and drug decision-making in cancer pain management remains challenges. This study aims to develop machine learning models using real-world clinical data and prior knowledge to support drug decision-making in cancer pain management. METHODS Clinical records from the Xiangya Hospital information system and a specialized cancer pain platform were used to develop two machine learning models: one for patients newly experiencing pain and one for patients with inadequate pain control. A total of 10,317 clinical records were used for model training, and 1,000 external records were obtained from the Cancer Hospital of the Chinese Academy of Medical Sciences for validation. Model performance was evaluated based on accuracy, AUC, and brier score. RESULTS Decision Tree and Gradient Boosting algorithms were selected for the two models, achieving an average accuracy of 98.47% and 94.74%, respectively, with AUCs of 99.62% and 94.74%. External validation accuracy was 97.4% and 93.1%, respectively, with AUCs of 99.83% and 97.01%. CONCLUSION The models proposed in this study can serve as decision support tools for healthcare professionals, assisting physicians in making optimized medication decisions in the absence of pharmacists.
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Affiliation(s)
- Lu Zhang
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, China; Institute for Rational and Safe Medication Practices, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China; The Hunan Institute of Practice and Clinical Research, China
| | - Hui-Min Yu
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, China; Institute for Rational and Safe Medication Practices, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China; The Hunan Institute of Practice and Clinical Research, China
| | - Jing-Yang Li
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, China; Institute for Rational and Safe Medication Practices, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China; The Hunan Institute of Practice and Clinical Research, China
| | - Ling Huang
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, China; Institute for Rational and Safe Medication Practices, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China; The Hunan Institute of Practice and Clinical Research, China
| | - Shu-Qiao Cheng
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, China; Institute for Rational and Safe Medication Practices, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China; The Hunan Institute of Practice and Clinical Research, China
| | - Jian Xiao
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, China; Institute for Rational and Safe Medication Practices, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China; The Hunan Institute of Practice and Clinical Research, China.
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