基于机器学习的预测,在2型糖尿病的首次胰岛素使用者中预测了药物补充的坚持
Ya-Lin Chen1, Phung-Anh Nguyen2, Chia-Hui Chien3
1Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA; Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.
Diabetes research and clinical practice
|December 4, 2023
概括
在2型糖尿病 (T2DM) 患者中预测胰岛素坚持是至关重要的. 这项研究开发了模型来识别可能不遵守的个人,帮助个性化护理以更好地管理糖尿病.
科学领域:
- 内分泌学和代谢性疾病.
- 药物经济学和健康研究成果研究成果
- 医疗保健中的人工智能
背景情况:
- 2型糖尿病 (T2DM) 的患病率正在上升,需要有效的长期管理策略.
- 坚持注射药物如胰岛素是不理想的,低于80%的门,影响治疗疗效.
- 识别患有不遵守风险的患者对于T2DM护理的积极干预至关重要.
研究的目的:
- 开发和验证分类模型,用于预测成人T2DM患者的胰岛素坚持性,这些患者刚开始胰岛素治疗.
- 在这个特定的患者群体中确定胰岛素不坚持的关键预测因素.
- 为医疗保健提供者创建一个潜在的临床决策支持工具.
主要方法:
- 利用了来自台北医科大学临床研究数据库 (TMUCRD) 的临床数据,涵盖了2004-2020年.
- 坚持被定义为至少80%的药物占有率 (MPR).
- 开发了两种XGBoost模型,使用跨人口统计,药物,并发症,实验室数据和医疗保健利用的预测因素.
主要成果:
- 预测模型实现了内部测试的ROC曲线下的面积 (AUROC) 为0.782,外部测试为0.771.
- SHAP分析确定了处方药的数量,门诊访问和实验室数据作为胰岛素坚持的重要预测指标.
- 研究队列包括来自台湾的4134名T2DM患者.
结论:
- 这项开创性的研究成功开发了成人T2DM患者胰岛素坚持的预测模型,这些患者开始使用胰岛素.
- 这些模型显示出作为临床决策支持工具的潜力,用于识别患有不遵守风险的患者.
- 这些发现可以指导医疗保健提供者设计定制的教育干预措施,以改善胰岛素坚持和糖尿病的结果.
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