论文标题
探索创伤复苏的运行时决策支持
Exploring Runtime Decision Support for Trauma Resuscitation
论文作者
论文摘要
基于AI的推荐系统已成功应用于许多域（例如，电子商务，提要排名）。医学专家认为，将这种方法纳入临床决策支持系统可能有助于减少医疗团队的错误并改善治疗过程中的患者结果（例如创伤复苏，手术过程）。但是，已经进行了有限的研究来开发自动数据驱动的治疗决策支持。我们探索了建立治疗建议系统以提供下一分钟活动预测的可行性。该系统使用患者环境（例如人口统计和生命体征）和过程上下文（例如活动）来连续预测将在下一分钟进行的活动。我们在预先录制的创伤复苏数据集上评估了我们的系统，并对不同模型变体进行了消融研究。最佳模型的平均F1得分为61种活动类型为0.67。我们包括医疗团队的反馈并讨论未来的工作。
AI-based recommender systems have been successfully applied in many domains (e.g., e-commerce, feeds ranking). Medical experts believe that incorporating such methods into a clinical decision support system may help reduce medical team errors and improve patient outcomes during treatment processes (e.g., trauma resuscitation, surgical processes). Limited research, however, has been done to develop automatic data-driven treatment decision support. We explored the feasibility of building a treatment recommender system to provide runtime next-minute activity predictions. The system uses patient context (e.g., demographics and vital signs) and process context (e.g., activities) to continuously predict activities that will be performed in the next minute. We evaluated our system on a pre-recorded dataset of trauma resuscitation and conducted an ablation study on different model variants. The best model achieved an average F1-score of 0.67 for 61 activity types. We include medical team feedback and discuss the future work.
