Exploring Runtime Decision Support for Trauma Resuscitation

Keyi Li, Sen Yang, Travis M Sullivan, Randall Burd, Ivan Marsic

Abstract:

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
prerecorded 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.