关于处理腹膜透析患者随访电子病历的协议,以训练人工智能模型
Tianlong Wang1, Yinghao Zhu1, Zixiang Wang1
1Peking University, Beijing, China.
STAR protocols
|October 2, 2024
概括
本研究介绍了一种处理腹膜透析患者电子病历的协议,以训练人工智能 (AI) 模型. 人工智能模型旨在预测一年死亡风险,改善末期病患者的护理.
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
- 人工智能的人工智能
背景情况:
- 缺乏标准化的协议,用于将末期病 (ESRD) 患者数据集成到AI模型中.
- 这种差距限制了人工智能在改善ESRD患者护理方面的应用.
研究的目的:
- 提出处理腹腔透析 (PD) 患者电子病历 (EMR) 的详细协议.
- 开发一套可用的数据集,用于训练人工智能模型,以预测PD患者的一年全因死亡风险.
主要方法:
- 协议开发环境设置和EMR的数据转换.
- 处理了来自1336名PD患者的1万多份随访记录.
- 为人工智能模型培训创建数据集,重点是死亡率预测.
主要成果:
- 为人工智能分析准备PD患者EMR数据的综合协议.
- 一个结构化的数据集,使人工智能模型开发能够预测一年死亡风险.
- 在ESRD中使用EMR数据用于预测AI的可行性.
结论:
- 提出的协议有助于将PD患者EMR数据集成到AI模型中.
- 这种方法增强了人工智能预测死亡风险和改善ESRD患者护理的潜力.
- 标准化数据处理对于在科中推进人工智能应用至关重要.
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