Health Life

New neural network helps doctors explain relapses of heart failure patients

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Patient data are a treasure trove for AI researchers. There’s a problem though: many algorithms used to mine patient data act as black boxes, which makes their predictions often hard to interpret for doctors. Researchers from Eindhoven University of Technology (TU/e) and the Zhejiang university in China have now developed an algorithm that not only predicts hospital readmissions of heart failure patients, but also tells you why these occur. The work has been published in BMC Medical Informatics and Decision Making.

Doctors are increasingly using data from electronic healthcare records to asses patient risks, predict outcomes, and recommend and evaluate treatments. Application of machine learning algorithms in clinical settings has however been hampered by lack of interpretability. The models often act as : you see what goes in (data) and what comes out (predictions), but you can’t see what happens in between. It can therefore be very hard to interpret why the models are saying what they are saying.

This undermines the trust healthcare professionals have in machine learning algorithms, and limits their use in everyday clinical decisions. Of course, interpretability is also a key requirement of EU privacy regulations (GDPR), so improving it also has legal benefits.

Attention-based neural networks

To solve this problem, Ph.D. candidate Peipei Chen of the Department of Industrial Engineering and Innovation Sciences, together with other researchers at TU/e and Zhejiang University in Hangzhou, has tested an attention-based on heart patients in China. Attention-based networks are able to focus on key details in data using contextual information.

New neural network helps doctors explain relapses of heart failure patients
In this figure we see the relative risk weights (y-axis) of 105 characteristics (x-axis) for two random patients. For patient a) features NT-proBNP (21), Sodium (32) and Coronary Heart Disease (12) are the most important risk factors, for patient b) NT-proBNP