一个基于主动对比学习的血液透析死亡率预测模型
Feng Wang1, Shengqiang Chi1, Xueyao Li1
1Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China.
Studies in health technology and informatics
|January 25, 2024
概括
预测血液透析 (HD) 患者的死亡风险至关重要. 使用电子健康记录 (EHR) 和主动对比学习 (ACL) 的新两阶段方法有效地识别高风险患者,改善护理.
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:
- 需要血液透析的末期病 (ESRD) 与高死亡率和严重的经济负担有关.
- 准确预测维持性疾病患者的死亡风险对于及时干预和改善患者结果至关重要.
研究的目的:
- 开发和验证一种新的两阶段协议,用于使用电子健康记录 (EHR) 数据预测维护性HD患者的死亡风险.
- 通过主动对比学习 (ACL) 方法提高多层感知子 (MLP) 模型的预测性能.
主要方法:
- 实施了两阶段的预测协议,从初始风险评估的MLP模型开始.
- 第二阶段采用了主动对比学习 (ACL) 方法来优化样本选择和表示空间,从而提高预测准确性.
主要成果:
- 与其他方法相比,拟议的ACL方法显示出更高的性能.
- 该模型的平均F1得分为0.820,接收器运行特征曲线 (AUC) 下的平均面积为0.853.
结论:
- 开发的两阶段协议,将MLP和ACL整合到EHR数据上,提供了一种有效的方法来预测HD患者的死亡风险.
- 这种方法可用于横截面EHR数据分析,并适用于预测其他疾病背景下的结果.
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