Diagnostic Prediction Using Discomfort Drawings
This work addresses pain diagnosis support for healthcare professionals, though it appears incremental as it adapts an existing multimodal model to a new medical application.
The paper tackles diagnostic prediction from patient discomfort drawings by extending the Inter-Battery Topic Model (IBTM) on a real-world dataset, achieving reasonable prediction accuracy for diagnostic labels.
In this paper, we explore the possibility to apply machine learning to make diagnostic predictions using discomfort drawings. A discomfort drawing is an intuitive way for patients to express discomfort and pain related symptoms. These drawings have proven to be an effective method to collect patient data and make diagnostic decisions in real-life practice. A dataset from real-world patient cases is collected for which medical experts provide diagnostic labels. Next, we extend a factorized multimodal topic model, Inter-Battery Topic Model (IBTM), to train a system that can make diagnostic predictions given an unseen discomfort drawing. Experimental results show reasonable predictions of diagnostic labels given an unseen discomfort drawing. The positive result indicates a significant potential of machine learning to be used for parts of the pain diagnostic process and to be a decision support system for physicians and other health care personnel.