A human-interpretable machine learning approach to predict mortality in severe mental illnesses
Publication: npj Schizophrenia
Banerjee S, Liò P, Jones PB, Cardinal RN
8 December 2021
Summary
Machine learning (ML), one aspect of artificial intelligence (AI), involves computer algorithms that train themselves. They have been widely applied in the healthcare domain. However, many trained ML algorithms operate as ‘black boxes’, producing a prediction from input data without a clear explanation of their workings.
Researchers apply class-contrastive counterfactual reasoning to ML to demonstrate how specific changes in inputs lead to different predictions of mortality in people with severe mental illness (SMI).
Researchers produce predictions accompanied by visual and textual explanations as to how the prediction would have differed given specific changes to the input.
