Diagnostic Prediction Using Discomfort Drawings

TL;DR

This paper investigates using machine learning, specifically an extended Inter-Battery Topic Model, to predict diagnoses from discomfort drawings, demonstrating promising results for aiding pain diagnosis in healthcare.

Contribution

It introduces an extension of the IBTM to predict diagnoses from discomfort drawings, showcasing the potential of machine learning in pain diagnostics.

Findings

Reasonable diagnostic predictions achieved

Machine learning shows promise as a decision support tool

Potential to assist healthcare professionals in 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 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.