使用临床变量,包括ICD-10代码,对人工智能模型进行外部验证,用于预测创伤患者的住院死亡率:多中心回顾性队列研究
Seungseok Lee1, Do Wan Kim2, Na-Eun Oh1
1Department of Biomedical Engineering, Kyung Hee University, 446-701 Electronic Information College Building, Kyunghee Univ, Global Campus, Seocheon-dong, Giheung-gu, Yongin-si, Gyeonggi, Republic of Korea.
Scientific reports
|January 8, 2025
概括
一个人工智能 (AI) 模型准确地预测了创伤患者的住院死亡率,超过了传统的评分系统. 这种人工智能工具对改善急诊室分拣和患者护理非常有希望.
科学领域:
- 医疗信息学 医疗信息学
- 创伤外科 手术 创伤外科
- 医疗保健中的人工智能
背景情况:
- 人工智能 (AI) 越来越多地被用于医疗保健,以提高患者护理和临床结果.
- 以前开发的AI模型使用ICD-10代码和临床变量预测创伤患者的住院死亡率.
研究的目的:
- 为了外部验证先前开发的AI模型的性能,用于预测创伤患者的住院死亡率.
- 为了比较人工智能模型的准确性与传统的评分系统,如伤害严重性评分 (ISS) 和基于ICD的ISS (ICISS).
主要方法:
- 采用了多中心回顾性队列研究设计,分析了2020年1月至2021年12月的数据.
- 该研究包括通过特定的ICD-10代码和其他临床变量识别的创伤患者.
- 使用灵敏度,特异性,精度,平衡精度,精度,F1得分和接收器操作特征曲线 (AUROC) 下的面积来评估性能.
主要成果:
- 人工智能模型实现了0.9448的高整体AUROC和85.08%的平衡精度,超过了ISS和ICISS.
- 该模型在不同医院和伤害严重程度 (ISS < 9和ISS ≥ 9) 中显示出一致的预测准确性.
- 对于严重的伤害 (ISS ≥ 9),该模型保持了高灵敏度 (93.60%) 和平衡的准确性 (77.08%).
结论:
- 外部验证证实了人工智能模型对创伤患者住院死亡风险的高预测准确性和可靠性.
- 人工智能模型在不同患者群体和伤害严重程度的有效性支持其潜在的整合到急诊室.
- 这种人工智能工具可以显著提高患者分拣和治疗方案,改善创伤患者的治疗结果.
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