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

Soumya Banerjee1, Pietro Lio2, Peter B Jones3,4

  • 1Department of Psychiatry, University of Cambridge, Cambridge, UK. sb2333@cam.ac.uk.

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Summary

This study introduces class-contrastive reasoning to make artificial intelligence (AI) predictions interpretable in severe mental illness (SMI) patients. It identifies factors like antidepressant use reducing mortality risk in schizophrenia patients.

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Area of Science:

  • Artificial Intelligence in Healthcare
  • Clinical Decision Support Systems
  • Machine Learning Interpretability

Background:

  • Machine learning (ML) models in healthcare often function as "black boxes," limiting clinical utility due to lack of transparency.
  • Interpretable predictions are crucial for justifiable clinical decisions, especially in public health challenges like severe mental illness (SMI).

Purpose of the Study:

  • To apply class-contrastive counterfactual reasoning to ML models for interpretable mortality risk prediction in patients with schizophrenia.
  • To demonstrate how specific input changes influence ML predictions and provide visual/textual explanations.

Main Methods:

  • Utilized routinely collected electronic healthcare record data from mental health services for patients with schizophrenia.
  • Structured data using a framework informed by clinical knowledge, encompassing physical health, mental health, and social factors.
  • Trained ML algorithms and statistical learning techniques, employing class-contrastive analysis for prediction explanation.

Main Results:

  • The ML algorithm achieved an AUROC of 0.80 for mortality prediction in schizophrenia patients.
  • Class-contrastive analysis identified factors associated with mortality risk, including antidepressant use (lower risk) and alcohol/drug abuse or delirium diagnosis (higher risk).
  • Highlighted the significant role of comorbidities in determining mortality and the need for their management.

Conclusions:

  • Class-contrastive reasoning offers a method for generating interpretable ML predictions from electronic health records for diseases of public health significance.
  • The findings underscore the importance of managing comorbidities and suggest potential therapeutic targets for interventions in schizophrenia patients.
  • This approach represents a step towards developing interpretable AI for managing schizophrenia and potentially other complex diseases.