A class-contrastive human-interpretable machine learning approach to predict mortality in severe mental illness.

ABSTRACT: 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. Non-transparent predictions are of limited utility in many clinical domains, where decisions must be justifiable. Here, we 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), a major public health challenge. We produce predictions accompanied by visual and textual explanations as to how the prediction would have differed given specific cha

SUBMITTER: Banerjee S 

PROVIDER: S-EPMC8654849 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature