开发一种机器学习工具,利用健康检查数据预测发生慢性病的风险
Yuki Yoshizaki1, Kiminori Kato2, Kazuya Fujihara3
1Department of Medical Informatics and Statistics, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan.
Frontiers in public health
|November 18, 2024
概括
机器学习模型有效地使用健康数据预测慢性病 (CKD) 风险在一年内,估计的膜过率 (eGFR) 是一个关键因素. 根据目前的数据,预测蛋白尿的发病仍然具有挑战性.
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:
- 慢性病 (CKD) 是一个重要的全球健康问题,需要早期检测.
- 早期发现CKD对于有效管理和预防严重后果至关重要.
- 这项研究解决了对CKD发展的预测工具的需求.
研究的目的:
- 开发和评估用于预测CKD风险的机器学习模型.
- 预测在1年和5年的时间框架内发展CKD的可能性.
- 评估各种健康指标对CKD预测的影响.
主要方法:
- 利用了30273名参与者 (2017-2022) 的健康检查数据.
- 开发了使用后勤回归,条件后勤回归,神经网络和循环神经网络的预测模型.
- 检查的结果包括事件估计的球过率 (eGFR) <60 mL/min/1.73 m2和蛋白尿的发展.
主要成果:
- 模型显示高预测值,灵敏度和特异性 (>0.8) 对于1年的CKD发病 (eGFR <60).
- 接收器运行特征曲线 (AUROC) 下的面积超过0.9,用于1年的eGFR预测.
- 五年预测AUROCs从0.889到0.890不等;没有eGFR作为变量,预测准确性显著下降.
结论:
- 机器学习模型在预测CKD风险方面表现有前途,特别是当包括eGFR时.
- 仅从健康检查数据中预测蛋白尿的发作存在挑战.
- 需要进一步的研究来改进对eGFR下降和尿蛋白增加的预测模型.
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