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

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

Authors :
Soumya Banerjee
Pietro Lio
Peter B. Jones
Rudolf N. Cardinal
Source :
npj Schizophrenia, Vol 7, Iss 1, Pp 1-13 (2021)
Publication Year :
2021
Publisher :
Nature Portfolio, 2021.

Abstract

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 changes to the input. We apply it to routinely collected data from a mental health secondary care provider in patients with schizophrenia. Using a data structuring framework informed by clinical knowledge, we captured information on physical health, mental health, and social predisposing factors. We then trained an ML algorithm and other statistical learning techniques to predict the risk of death. The ML algorithm predicted mortality with an area under receiver operating characteristic curve (AUROC) of 0.80 (95% confidence intervals [0.78, 0.82]). We used class-contrastive analysis to produce explanations for the model predictions. We outline the scenarios in which class-contrastive analysis is likely to be successful in producing explanations for model predictions. Our aim is not to advocate for a particular model but show an application of the class-contrastive analysis technique to electronic healthcare record data for a disease of public health significance. In patients with schizophrenia, our work suggests that use or prescription of medications like antidepressants was associated with lower risk of death. Abuse of alcohol/drugs and a diagnosis of delirium were associated with higher risk of death. Our ML models highlight the role of co-morbidities in determining mortality in patients with schizophrenia and the need to manage co-morbidities in these patients. We hope that some of these bio-social factors can be targeted therapeutically by either patient-level or service-level interventions. Our approach combines clinical knowledge, health data, and statistical learning, to make predictions interpretable to clinicians using class-contrastive reasoning. This is a step towards interpretable AI in the management of patients with schizophrenia and potentially other diseases.

Subjects

Subjects :
Psychiatry
RC435-571

Details

Language :
English
ISSN :
2334265X
Volume :
7
Issue :
1
Database :
Directory of Open Access Journals
Journal :
npj Schizophrenia
Publication Type :
Academic Journal
Accession number :
edsdoj.f80a5586c03a4c58a1f6732a5c71fb54
Document Type :
article
Full Text :
https://doi.org/10.1038/s41537-021-00191-y