Human intention recognition for trauma resuscitation: An interpretable deep learning approach for medical process data.

Abstract

Objective: Trauma resuscitation is the initial evaluation and management of injured patients in the emergency department. This time-critical process requires the simultaneous pursuit of multiple resuscitation goals. Recognizing whether the required goal is being pursued can reduce errors in goal-related task performance and improve patient outcomes. The intention to pursue a goal can often be inferred from ongoing and completed treatment activities, but monitoring goal pursuit is cognitively demanding and prone to errors. We introduced an interpretable deep learning-based approach to aid decision making by automatically recognizing goal pursuit during trauma resuscitation.

Methods: We developed a predictive model to recognize the pursuit of two resuscitation goals: airway stabilization and circulatory support. We used event logs of 381 pediatric trauma resuscitations from August 2014 to November 2022 to train a neural network model with a dual-GRU structure that learns from both time-level and activity-type-level features. Our model makes predictions based on a sequence of activities and corresponding timestamps. To enhance the model and facilitate interpretation of predictions, we used the attention weights assigned by our model to represent the importance of features. These weights identified the critical time points and contributing activities during a goal pursuit.

Results: Our model achieved an average area under the receiver operating characteristic curve (AUC) score of 0.84 for recognizing airway stabilization and 0.83 for recognizing circulatory support. The most contributing activities and timestamps were aligned with domain knowledge.

Conclusion: Our interpretable predictive model can recognize provider intention based on a limited number of treatment activities. The model outperformed existing predictive models for medical events in accuracy and in interpretability. Integrating our model into a decision-support system would automate the tracking of provider actions, optimizing workflow to ensure timely delivery of care.