Diagnostic Prediction Using Discomfort Drawings with IBTM
Abstract
In this paper, we explore the possibility to apply machine learning to make diagnostic predictions using discomfort drawings. Discomfort drawings have proven to be an effective method to collect patient data and make diagnostic decisions in real-life practice. A dataset from relevant patient cases is collected for which medical experts provide diagnostic labels. Next, we use 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. Additionally, we generate synthetic discomfort drawings with IBTM given a diagnostic label, which results in typical cases of symptoms. 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.