  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.
