一个人工智能应用程序来预测在关键骨科创伤患者中对机械通风的长期依赖:一个建立和验证研究
Weigang Jiang1, Tao Liu2, Baisheng Sun3,4
1Department of Orthopedics, The Fourth Affiliated Hospital of Soochow University (Suzhou Dushu Lake Hospital), Suzhou City, 215000, Jiang Su Province, People's Republic of China.
BMC musculoskeletal disorders
|December 30, 2024
概括
开发了一个人工智能 (AI) 平台,用于预测关键骨科创伤患者的长时间机械通风. 人工智能模型准确地识别高风险个体,帮助优化患者护理和资源配置.
科学领域:
- 关键护理医学 关键护理医学
- 医疗人工智能的人工智能
- 骨科创伤管理 骨科创伤管理
背景情况:
- 长时间的机械通风给ICU带来了挑战,影响了患者的治疗结果和资源使用.
- 预测长时间的机械通风对于预防并发症和指导干预至关重要.
- 需要有预测工具,特别是在关键骨科创伤患者中.
研究的目的:
- 建立和验证一个人工智能 (AI) 平台.
- 评估关键骨科创伤患者长期依赖机械通风的情况.
主要方法:
- 分析了1400名急性骨科创伤患者,需要机械通风 (≥7天).
- 使用培训和验证队伍开发和验证机器学习模型 (例如,eXGBM,LightGBM,RF).
- 外部验证了最佳模型并将其部署为在线AI计算器.
主要成果:
- 极端梯度增强机 (eXGBM) 模型表现出卓越的性能.
- 确定的主要预测因素包括呼吸速率,下肢骨折,葡萄糖,PaO2和PaCO2.
- 人工智能平台成功预测了长时间的机械通风,有助于风险分层和干预建议.
结论:
- 人工智能模型是一个有价值的工具,用于对长期机械通风的高风险患者进行分层.
- 这种人工智能方法为优化重症监护和资源配置提供了一个有希望的策略.
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