Improving the accuracy of medical diagnosis with causal machine learning

Jonathan G. Richens (), Ciarán M. Lee and Saurabh Johri
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Jonathan G. Richens: Babylon Health, 60 Sloane Ave
Ciarán M. Lee: Babylon Health, 60 Sloane Ave
Saurabh Johri: Babylon Health, 60 Sloane Ave

Nature Communications, 2020, vol. 11, issue 1, 1-9

Abstract: Abstract Machine learning promises to revolutionize clinical decision making and diagnosis. In medical diagnosis a doctor aims to explain a patient’s symptoms by determining the diseases causing them. However, existing machine learning approaches to diagnosis are purely associative, identifying diseases that are strongly correlated with a patients symptoms. We show that this inability to disentangle correlation from causation can result in sub-optimal or dangerous diagnoses. To overcome this, we reformulate diagnosis as a counterfactual inference task and derive counterfactual diagnostic algorithms. We compare our counterfactual algorithms to the standard associative algorithm and 44 doctors using a test set of clinical vignettes. While the associative algorithm achieves an accuracy placing in the top 48% of doctors in our cohort, our counterfactual algorithm places in the top 25% of doctors, achieving expert clinical accuracy. Our results show that causal reasoning is a vital missing ingredient for applying machine learning to medical diagnosis.

Date: 2020
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DOI: 10.1038/s41467-020-17419-7

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