Journal article
Human intention recognition for trauma resuscitation: An interpretable deep learning approach for medical process data
Journal of biomedical informatics, v 161, 104767
31 Dec 2024
PMID: 39746431
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
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.
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.
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.
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.
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Details
- Title
- Human intention recognition for trauma resuscitation: An interpretable deep learning approach for medical process data
- Creators
- Keyi Li - Rutgers, The State University of New JerseyMary S Kim - Children's NationalWenjin Zhang - Rutgers, The State University of New JerseySen Yang - Waymo, Mountain View, CA, USA. Electronic address: sy358@scarletmail.rutgers.eduGenevieve J Sippel - Children's NationalAleksandra Sarcevic - Drexel UniversityRandall S Burd - Children's NationalIvan Marsic - Rutgers, The State University of New Jersey
- Publication Details
- Journal of biomedical informatics, v 161, 104767
- Publisher
- ACADEMIC PRESS INC ELSEVIER SCIENCE
- Number of pages
- 14
- Grant note
- U.S. National Institutes of Health/National Library of Medicine: R01LM011834
This work is supported by the U.S. National Institutes of Health/National Library of Medicine under grant number R01LM011834.
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:001403091900001
- Scopus ID
- 2-s2.0-85213838600
- Other Identifier
- 991022016381504721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Interdisciplinary Applications
- Medical Informatics