SepsisCalc:将临床计算器集成到通过动态时间图构建的早期败血症预测中
Changchang Yin1, Shihan Fu2, Bingsheng Yao2
1The Ohio State University, Columbus, Ohio, USA.
KDD : proceedings. International Conference on Knowledge Discovery & Data Mining
|April 17, 2025
概括
这项研究介绍了SepsisCalc,这是一个新的框架,将临床计算器集成到人工智能 (AI) 模型中,以实现更透明和更准确的败血症预测. 人工智能工具有助于临床医生在早期的败血症识别和干预计划.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 临界护理医学 临界护理医学
背景情况:
- 败血症是由于感染而导致的危及生命的器官功能障碍,需要早期检测以改善患者的结果.
- 临床计算器对于败血症识别至关重要,但当前的人工智能模型缺乏这种整合,从而降低了透明度.
- 现有的AI败血症预测模型通常提供单一的风险评分,未能纳入对临床决策至关重要的器官功能障碍评估.
研究的目的:
- 开发一个新的框架,SepsisCalc,将临床计算器集成到AI败血症预测模型中.
- 通过模仿临床医生的工作流程来提高AI败血症预测的透明度和精度.
- 为临床部署创建一个人-人工智能交互工具,以支持早期干预和决策.
主要方法:
- 将电子健康记录 (EHR) 作为时间图表来处理缺失的变量.
- 整合一个学习模块,以动态地将估计的计算器值纳入时间图中.
- 开发一个新的框架,SepsisCalc,将AI预测与临床计算器评估结合起来.
主要成果:
- 拟议的SepsisCalc模型在败血症预测任务中,与最先进的方法相比,表现优越.
- 在真实世界数据集上的实验结果验证了模型的有效性.
- 开发了一种人-人工智能交互系统,以帮助临床医生理解预测和规划干预措施.
结论:
- SepsisCalc通过整合已建立的临床计算器,为人工智能驱动的败血症预测提供了临床透明和精确的方法.
- 该框架通过时间图表表示和动态学习来解决电子健康记录中缺少数据的挑战.
- 开发的系统提供可操作的临床决策支持,为器官功能障碍和潜在的败血症提供及时干预.
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