Modeling Physicians' Utterances to Explore Diagnostic Decision-making

Modeling Physicians' Utterances to Explore Diagnostic Decision-making

Xuan Guo, Rui Li, Qi Yu, Anne Haake

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 3700-3706. https://doi.org/10.24963/ijcai.2017/517

Diagnostic error prevention is a long-established but specialized topic in clinical and psychological research. In this paper, we contribute to the field by exploring diagnostic decision-making via modeling physicians' utterances of medical concepts during image-based diagnoses. We conduct experiments to collect verbal narratives from dermatologists while they are examining and describing dermatology images towards diagnoses. We propose a hierarchical probabilistic framework to learn domain-specific patterns from the medical concepts in these narratives. The discovered patterns match the diagnostic units of thought identified by domain experts. These meaningful patterns uncover physicians' diagnostic decision-making processes while parsing the image content. Our evaluation shows that these patterns provide key information to classify narratives by diagnostic correctness levels.
Keywords:
Multidisciplinary Topics and Applications: Cognitive Modeling
Natural Language Processing: Natural Language Semantics