值得信赖和不确定性意识的人工智能用于预测部和膝盖关节整形术后的呼吸道并发症
Farnaz Rezvani1, Kate Towsen2, Zoe Menezes2
1Department of Computer Science, Brunel University of London, London, United Kingdom.
AMIA ... Annual Symposium proceedings. AMIA Symposium
|February 23, 2026
概括
一个AI框架预测关节和膝盖置换手术后的主要呼吸道并发症. 这个值得信赖的AI工具量化了预测的不确定性,提高了患者的安全性,并为全关节整形术 (THA) 和全膝关节整形术 (TKA) 患者提供了个性化的护理.
科学领域:
- 整形外科手术 整形外科手术
- 人工智能在医学中的应用
- 肺部医学 肺部医学
背景情况:
- 全关节整形术 (THA) 和全膝关节整形术 (TKA) 是越来越常见的手术.
- 这些手术存在风险,特别是主要的呼吸道并发症.
- 早期识别高风险患者对于安全性和结果至关重要.
研究的目的:
- 开发一种人工智能框架,用于预测THA/TKA后的主要呼吸道并发症.
- 创建一个不确定性意识和可解释的风险预测模型.
- 提高手术前治疗和患者安全.
主要方法:
- 评估了四种机器学习模型:随机森林 (RF),XGBoost (XGB),物流回归 (LR) 和人工神经网络 (ANN).
- 预计在住院期间和THA/TKA后30天内出现主要呼吸道并发症 (计划外输管,通风衰竭,肺炎).
- 评估模型性能使用像F1分数这样的指标.
主要成果:
- 机器学习模型显示了呼吸道并发症的强大预测性能.
- 对于THA呼吸道并发症,RF获得了0.87的F1得分.
- 在TKA呼吸道并发症方面,ANN实现了0.87的F1得分,优于其他模型.
结论:
- 拟议的AI框架准确地预测了THA/TKA.之后的主要呼吸道并发症.
- 不确定性意识的方法提高了解释性,并支持个性化的临床决策.
- 这种人工智能工具可以提高患者安全,并优化关节置换手术患者的护理.
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