阿德莱德得分:一种人工智能测量一般手术后准备出院的准备
Joshua G Kovoor1,2,3, Stephen Bacchi3,4,5,6, Aashray K Gupta3,4,7
1Queen Elizabeth Hospital, University of Adelaide, Adelaide, South Australia, Australia.
ANZ journal of surgery
|June 2, 2023
概括
机器学习算法准确地预测患者在整体手术后12至24小时内准备出院. 阿德莱德评分为手术团队提供了一种有价值的工具,以优化患者流量和资源配置.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 手术结果研究研究.
背景情况:
- 评估患者出院准备度对于高效的医院运营至关重要.
- 一般手术患者需要及时准确地预测出院情况.
- 目前用于预测放电的方法可能缺乏准确性和及时性.
研究的目的:
- 评估机器学习算法来预测一般外科病人的出院情况.
- 开发一个可靠的12小时和24小时内排放准备度的衡量标准.
- 评估随机森林,XGBoost和物流回归模型的性能.
主要方法:
- 利用了两年来在两个三级医院连续两次全科医生手术患者的数据.
- 将观察和实验室数据分层化为培训,测试和验证套件.
- 评估了随机森林,XGBoost和后勤回归模型,使用病房圆周笔记时间.
主要成果:
- 机器学习模型在12小时和24小时内预测放电时显示出高准确度.
- 随机森林在验证数据上实现了最高的分类准确性 (12小时0.85,24小时0.84).
- 算法生成了一个连续的分数,表明准备好放电.
结论:
- 一种人工智能测量,阿德莱德分数,有效地预测一般外科手术的排放准备.
- 这种由人工智能驱动的得分可以帮助临床决策制定出院计划.
- 阿德莱德评分有可能提高外科系统的效率.
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