利用人工智能识别术后喉疼痛的高风险患者:一项观察性研究
Qiangqiang Zhou1, Xiaoya Liu2, Huifang Yun1
1Department of Anesthesiology, The Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Changzhou, Jiangsu, China.
Biomolecules & biomedicine
|October 23, 2023
概括
一个机器学习模型准确地预测了在接受全身麻醉的患者手术后喉疼痛 (POST). 这种预测工具可以识别高风险个体,帮助预防这种常见的并发症.
科学领域:
- 麻醉学和外科手术期间的医学.
- 医疗保健中的人工智能
- 预测分析是一种预测分析.
背景情况:
- 手术后喉痛 (POST) 是在全身麻醉后的常见并发症.
- 识别高风险患者对于有效的POST预防策略至关重要.
研究的目的:
- 开发和验证用于预测POST的机器学习模型.
- 为了比较随机森林 (RF),神经网络 (NN) 和极端梯度增强 (XGBoost) 模型的性能.
- 为了确定POST的关键预测因素.
主要方法:
- 利用了从834名接受全身麻醉与内内输管治疗的患者的数据.
- 使用培训,验证和外部队伍开发和验证模型.
- 使用本地可解释的模型不可知解释 (LIME) 来解释模型的结果.
主要成果:
- 神经网络 (NN) 模型在外部验证中表现出卓越的性能,实现了0.81的AUROC和0.77的AUPRC.
- 发现的关键预测因素包括年龄,性别,内管袖口压力,插入深度和输出管后的水分定时.
- 该NN模型显示了良好的校准和临床实用性.
结论:
- 该NN模型是预测POST的可靠工具,性能优于RF和XGBoost.
- 这种预测模型在医疗保健中有潜在的应用,以减少POST发生率.
- 针对模型中确定的高风险患者,可以改善患者的治疗结果.
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