Exploring Runtime Decision Support for Trauma Resuscitation
This addresses the need to reduce errors and improve outcomes in medical treatment processes, but it is incremental as it applies existing AI methods to a new clinical domain.
The paper tackled the problem of providing runtime decision support for trauma resuscitation by developing a treatment recommender system that predicts next-minute activities, achieving an average F1-score of 0.67 for 61 activity types.
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.