Causality Refined Diagnostic Prediction

TL;DR

This paper investigates how causal relationships can enhance diagnostic predictions in healthcare, using discomfort drawings to infer causal links among diagnoses and refine prediction accuracy, thereby aiding interpretability and decision-making.

Contribution

It introduces a method to infer causal relationships among diagnostic labels and employs these relationships to improve predictive accuracy in healthcare diagnostics.

Findings

Causal identification accurately detects relationships among diagnostic labels.

Using causal relationships can improve pain diagnostic prediction accuracy.

Causal inference provides interpretable insights for healthcare decision making.

Abstract

Applying machine learning in the health care domain has shown promising results in recent years. Interpretable outputs from learning algorithms are desirable for decision making by health care personnel. In this work, we explore the possibility of utilizing causal relationships to refine diagnostic prediction. We focus on the task of diagnostic prediction using discomfort drawings, and explore two ways to employ causal identification to improve the diagnostic results. Firstly, we use causal identification to infer the causal relationships among diagnostic labels which, by itself, provides interpretable results to aid the decision making and training of health care personnel. Secondly, we suggest a post-processing approach where the inferred causal relationships are used to refine the prediction accuracy of a multi-view probabilistic model. Experimental results show firstly that causal identification is capable of detecting the causal relationships among diagnostic labels correctly, and secondly that there is potential for improving pain diagnostics prediction accuracy using the causal relationships.