使用机器学习来预测慢性病患者的药物治疗问题
Alaa A Alghwiri1, Melanie R Weltman2, Linda-Marie U Lavenburg3
1Renal-Electrolyte Division, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA, alaa.alghwiri84@gmail.com.
American journal of nephrology
|June 17, 2025
概括
机器学习准确地识别了患有慢性病 (CKD) 的患者,这些患者有很高的药物治疗问题 (MTP) 风险. 该工具使用常规临床数据来预测MTP,帮助初级保健管理.
科学领域:
- 药物基因组学 药物基因组学
- 腎臟病學 (nephrology) 是一種醫學專業.
- 人工智能在医学中的应用
背景情况:
- 患有慢性病 (CKD) 的患者面临着大量的并发病和药物负担,增加了药物治疗问题的风险 (MTP).
- 协调健康管理伙伴关系 (CHAMP) 试验提供了数据,以开发预测模型,以预测MTP风险的CKD患者在初级保健.
研究的目的:
- 开发和验证一种机器学习模型,用于预测慢性病患者的药物治疗问题 (MTP).
- 在初级保健环境中确定与CKD人口中MTP风险相关的关键临床因素.
主要方法:
- 利用脏CHAMP试验中的730名患者的基线数据,分为80%的训练和20%的测试集.
- 评估了八个候选机器学习模型,选择了前三个 (随机森林,支持矢量机器,梯度增强) 以AUROC为基础进行改进.
- 最好的随机森林模型 (AUROC 0.72) 通过使用SHapley添加式扩展来确定预测因素的重要性.
主要成果:
- 在CKD患者中,很大一部分 (77.5%) 在基线时至少经历过一次MTP.
- 随机森林模型表现出良好的预测性能,AUROC为0.72,灵敏度为0.80,特异性为0.64.
- 对MTP风险的关键预测因素包括糖尿病状况,血红蛋白A1C,尿液中白蛋白与肌素的比率,静脉血压和年龄.
结论:
- 基于机器学习的风险计算器,利用常规可用的临床数据,可以有效地识别在门诊初级保健中患MTP高风险的CKD患者.
- 这种预测工具支持高风险个体的积极药物管理和干预,可能改善患者的治疗结果.
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