改善心力衰竭治疗决策:可解释的机器学习模型用于使用EHR数据进行先进治疗资格预测
Yufeng Zhang1, Jessica R Golbus2, Emily Wittrup3
1Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, 48103, MI, USA. chloezh@umich.edu.
BMC medical informatics and decision making
|February 14, 2024
概括
新的模型预测心力衰竭患者需要使用电子健康记录进行先进治疗. 这些模型改善了患者的治疗结果,并确定了关键的风险因素,以更好地管理心力衰竭.
科学领域:
- 心脏病学 心脏病学
- 生物医学信息学 生物医学信息学
- 机器学习 机器学习
背景情况:
- 及时转诊为高级心力衰竭疗法对于患者的结果至关重要.
- 当前的决策是复杂的,耗时的,需要专门的专业知识.
- 电子健康记录 (EHR) 包含有价值的序列数据用于预测.
研究的目的:
- 开发和评估逻辑张量回归 (LTR) 模型,用于预测需要先进疗法的心力衰竭患者.
- 通过使用EHR数据,分析人口和个人层面的模型性能.
- 确定与先进治疗转诊相关的关键临床特征和并发症.
主要方法:
- 提出了两种LTR模型 (标准和个性化) 使用不规则间隔的顺序EHR数据.
- 从之前的访问中收集了临床特征,以便在随后的访问开始时进行预测.
- 进行了针对患者的十倍交叉验证,并与其他机器学习模型进行了比较.
主要成果:
- 标准LTR的平均F1得分为0.708,AUC为0.903,AUPRC为0.836,平均LTR的平均F1得分为0.708,AUC为0.903,AUPRC的平均AUPRC为0.836.
- 个性化的LTR获得F1得分为0.670,AUC为0.869和AUPRC为0.839.
- LTR模型的性能优于其他机器学习方法,并通过重量转移提高了其性能.
结论:
- 拟议的LTR模型有效地预测了使用EHR数据进行先进心力衰竭治疗的需求.
- 确定了重要的临床特征,如慢性病和低血压,经过专家的验证.
- 这些模型提供了对人口和个人水平心力衰竭进展的风险因素的见解.
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