Interpretable Machine Learning Models for Assisting Clinicians in the Analysis of Physiological Data

Abstract

The analysis of physiological data plays a significant role in medical diagnostics. While state-of-the-art machine learning models demonstrate high levels of performance in classifying physiological data clinicians are slow to adopt them. A contributing factor to the slow rate of adoption is the “black-box” nature of the underlying model whereby the clinician is presented with a prediction result, but the rationale for that result omitted or not presented in an interpretable manner. This gives rise to the need for interpretable machine learning models such that clinicians can verify, and rationalise, the predictions made by a model. If a clinician understands why a model makes a prediction, they will be more inclined to accept a models assistance in analysing physiological data. This paper discusses some of the latest findings in interpretable machine learning. Thereafter, based on these findings, three models are selected and implemented to analyse ECG data that are both accurate and exhibit a high level of interpretability.

Paper ResearchGate

Recommended citation: P. Nisha, Urja Pawar and Ruairi O'Reilly (2019). “Interpretable Machine Learning Models for Assisting Clinicians in the Analysis of Physiological Data.” The 27th Irish Conference on Artificial Intelligence and Cognitive Science.
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