使用机器学习预测重症患者急性损伤的结果
Fateme Nateghi Haredasht1,2, Liesbeth Viaene3, Hans Pottel4
1KU Leuven, Campus KULAK - Department of Public Health and Primary Care, Etienne Sabbelaan 53, 8500, Kortrijk, Belgium. fateme.nateghi@kuleuven.be.
Scientific reports
|June 18, 2023
概括
机器学习模型准确地预测慢性病 (CKD) 和病情危急的急性损伤 (AKI) 第三阶段患者的死亡率. 这些人工智能工具有助于为严重的AKI结果做出临床决策.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 关键护理医学 关键护理医学
- 医疗保健中的人工智能
背景情况:
- 急性损伤 (AKI) 是重症监护病房 (ICU) 中的一种危急状况,与慢性病 (CKD) 和死亡率有关.
- 预测AKI第3期患者的结果对于临床管理和患者预后至关重要.
研究的目的:
- 开发和评估机器学习模型,用于预测 AKI 3 期 ICU 患者的CKD 发展和死亡率.
- 将机器学习模型的性能与传统的统计方法进行比较.
主要方法:
- 一项前性观察性研究,包括101名患有AKI第三阶段的ICU患者.
- 使用随机森林用于CKD和随机生存森林/XGBoost用于死亡率的预测模型的开发.
- 包括未标记的数据来增强生存分析培训.
主要成果:
- 随机森林模型在预测CKD方面表现强 (AUPR:0.895和0.848).
- 与基线COXPH相比,XGBoost模型在死亡率预测 (c指数:0.8248) 中表现优越.
- 纳入未标记的数据改善了生存分析的性能.
结论:
- 机器学习模型可以更好地预测严重的CKD患者的CKD和死亡率.
- 这些人工智能驱动的工具可以支持临床决策,用于管理AKI后的重症患者.
- 该研究强调了在生存预测任务中使用未标记数据的好处.
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