机器学习模型使用24小时尿液检测和电子健康记录衍生功能预测结石复发
Patrick Doyle1, Wu Gong2, Ryan Hsi2
1Vanderbilt University.
Research square
|July 18, 2023
概括
机器学习模型可以预测石的复发. 最少绝对收缩和选择操作员回归 (LASSO) 模型显示了预测症状石复发的最佳准确性.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 石复发对健康造成重大负担.
- 准确预测复发对于患者管理至关重要.
- 电子健康记录 (EHR) 为预测建模提供了丰富的数据来源.
研究的目的:
- 评估机器学习 (ML) 模型在预测症状性石复发中的准确性.
- 为了比较不同的ML模型与后勤回归 (LR) 的性能.
- 从EHR数据和24小时尿液分析中确定结石复发的关键预测因素.
主要方法:
- 训练了三个ML模型:LASSO,随机森林 (RF) 和XGBoost.
- 利用了来自EHR的特征和1231名患者的24小时尿液数据.
- 将ML模型性能与LR进行比较,使用接收器操作曲线下的面积 (AUC-ROC).
- 进行了手动回顾,以确定症状性石头事件.
主要成果:
- 症状结石复发率在2年后为25%,在5年后为31%.
- 拉索模型实现了预测复发的最高AUC (2年:0.62,5年:0.63).
- 患者的年龄,BMI和痛风病史被LASSO模型确定为重要的预测因素.
结论:
- 在预测石复发方面,ML模型,特别是LASSO,在预测石复发方面表现与LR相当或优于LR.
- 进一步的研究应该探索24小时尿液特征的整合,以提高模型的准确性.
- 这些发现支持了ML对个性化结石复发风险评估的潜力.
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