基于机器学习算法构建术后谵妄风险预测模型的研究进展

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武警医学 ›› 2024, Vol. 35 ›› Issue (2) : 181-184.

综述

基于机器学习算法构建术后谵妄风险预测模型的研究进展

高升润¹, 李芸², 李雨衡¹, 王艳庆¹, 高成杰¹

作者信息 +

Research progress in constructing postoperative delirium risk prediction model based on machine learning algorithm

GAO Shengrun, LI Yun, LI Yuheng, et al

Author information +

文章历史 +

摘要

术后谵妄(POD)是老年患者术后常见的严重并发症,表现为注意力及认知功能急性障碍,可导致住院时间延长、死亡率增高,并且增加个人与社会的经济负担。术前早期预测POD有助于减少或逆转其发生,对改善患者预后及减轻社会负担具有重要意义。近年来机器学习的深度发展促使更加高效可靠的POD预测模型不断发展完善,极大提高了POD风险的预防及临床诊疗水平,笔者对基于机器学习构建术后谵妄预测风险模型进展作一综述。

关键词

机器学习 / 谵妄 / 术后谵妄 / 预测模型

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高升润, 李芸, 李雨衡, 王艳庆, 高成杰. 基于机器学习算法构建术后谵妄风险预测模型的研究进展[J]. 武警医学. 2024, 35(2): 181-184

GAO Shengrun, LI Yun, LI Yuheng, et al　. Research progress in constructing postoperative delirium risk prediction model based on machine learning algorithm[J]. Medical Journal of the Chinese People Armed Police Forces. 2024, 35(2): 181-184

中图分类号： R614

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