Diagnostic Prediction Using Discomfort Drawings with IBTM

TL;DR

This paper demonstrates that machine learning, specifically the IBTM model, can effectively predict diagnostic labels from discomfort drawings, supporting clinical decision-making and generating synthetic symptom data.

Contribution

It introduces a novel application of the IBTM model to diagnose and generate discomfort drawings from patient data, advancing diagnostic support tools.

Findings

Reasonable diagnostic predictions achieved from discomfort drawings

Synthetic discomfort drawings successfully generated for given diagnoses

Machine learning shows potential as a decision support system in pain diagnosis

Abstract

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 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. The number of output diagnostic labels is determined by using mean-shift clustering on the 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.