  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.
