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机器学习模型使用韩国住院患者的临床信息来预测华法林释放剂量

Heejung Choi¹, Hee Jun Kang², Imjin Ahn³

¹Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43 gil, Songpa-gu, Seoul, 05505, Republic of Korea.

Scientific reports

|December 17, 2023

概括

机器学习模型准确地预测了华法林释放剂量,超过了医生的估计. 这有助于为患者提供个性化剂量,减少不良药物事件并改善最初的华法林治疗.

科学领域:

药物基因组学和计算生物学
临床信息学和数据科学

背景情况:

由于个体的变化和狭窄的治疗窗口,华法林的剂量具有挑战性.
华法林过量服用的不良药物事件可能是关键的,需要仔细调整剂量.
目前的方法涉及频繁的INR监测,这可能是耗时的.

研究的目的:

开发和验证用于预测初始华法林剂量的机器学习 (ML) 模型.
利用电子医疗记录中的临床数据进行个性化的华法林治疗.
将ML模型的预测精度与医生估计进行比较.

主要方法:

使用阿桑医疗中心 (n=3168) 和MIMIC-III (n=891) 的电子病历进行了回顾性研究.
开发了四种ML模型 (XGBoost,ANN,随机森林,线性回归) 来预测华法林释放剂量.
根据临床相关性选择的预测变量,包括出血风险.

主要成果:

与医生相比,ML模型显示出更高的预测准确度 (MAE:0.9-1.0与1.3).
XGBoost和人工神经网络模型实现了最低的平均绝对误差 (MAE) 0.9.9.
内部和外部验证证实了模型的预测性能.

结论:

ML模型可以有效地预测华法林释放剂量,作为初始剂量指南.
这些模型在初始住院期间提供了比基于医生的估计更好的准确性.
建议对这些ML模型进行进一步的外部验证,以使其在临床上得到广泛应用.

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