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Updated: Jan 13, 2026

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超越合规性:通过可解释的机器学习来解释患者风险,ERAS遵守和术后结果.

J Ripollés-Melchor1, Á V Espinosa2, A Abad-Motos3

  • 1Department of Anaesthesia and Critical Care, Infanta Leonor University Hospital, Madrid, Spain; Complutense University of Madrid, Madrid, Spain; Fluid Therapy and Hemodynamic Monitoring Working Group, Spanish Society of Anaesthesia and Critical Care, Madrid, Spain.

Revista espanola de anestesiologia y reanimacion
|January 9, 2026
PubMed
概括

手术后增强恢复 (ERAS) 协议显示了可变的坚持. 机器学习确定了患者的脆弱性和特定的ERAS项目作为并发症的关键预测因素,从而实现了量身定制的外科康复策略.

关键词:
学习是自动化的.这是一次色直肠外科手术.结肠直肠手术 结肠直肠手术在手术后出现的并发症.在手术后改善恢复.这就是脆弱的脆弱.脆弱性 脆弱性 脆弱性机器学习 机器学习改善手术后的康复情况.术后并发症 术后并发症

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