开发和验证基于人工智能的分拣支持算法,以预防内日分析性低血压
Federica Gervasoni1, Francesco Bellocchio1, Jaroslav Rosenberger2,3
1Fresenius Medical Care Italia SpA, Palazzo Pignano, Italy.
Journal of nephrology
|September 14, 2023
概括
人工智能模型现在可以预测内透析性低血压,这是一个常见的透析并发症. 这些工具使用常规数据来识别有风险的患者,从而实现个性化的预防策略,以获得更好的患者结果.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學.
- 人工智能的人工智能
- 预测分析是一种预测分析.
背景情况:
- 内透析性低血压是透析期间经常出现的并发症.
- 这种情况的管理不良可能导致不良结果.
- 个性化预防策略对于管理日内分析性低血压至关重要.
研究的目的:
- 开发和验证两种基于人工智能的风险模型.
- 为了预测不同时间点出现症状性内透析性低血压的发生.
主要方法:
- 使用XGBoost算法构建预测模型.
- 模型预测下一次透析治疗和下一个月的内透析性低血压.
- 来自EuCliD®数据库的数据用于模型推导,培训和验证.
主要成果:
- 这项研究分析了1,249,813次透析;内透析性低血压的发生率为10.07%.
- 人工智能模型表现出良好的区分 (AUC ~ 0.8) 和校准.
- 关键预测因素包括血压动态和血液动力学不稳定性指标.
结论:
- 常规收集的数据可以可靠地预测日内分析性低血压.
- 人工智能模型允许使用标准临床护理数据准确预测风险.
- 临床实施使得个性化,基于风险的干预措施能够预防和管理静脉透析性低血压.
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