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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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。

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关键词 ：机器学习, 谵妄, 术后谵妄, 预测模型

收稿日期: 2023-09-11

ZTFLH:

R614

基金资助:山东省自然科学基金青年项目(ZR2021QH031)

通讯作者: 高成杰,E-mail:gaochj@hotmail.com

作者简介: 高升润,硕士研究生,主治医师。

引用本文:

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. Med. J. Chin. Peop. Armed Poli. Forc., 2024, 35(2): 181-184.

链接本文:

http://journal08.magtechjournal.com/Jwk_wjyx/CN/ 或 http://journal08.magtechjournal.com/Jwk_wjyx/CN/Y2024/V35/I2/181

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