University of Pittsburgh

Patient-Specific Explanations for Predictions of Clinical Outcomes

graduate student
Friday, April 6, 2018 - 12:30pm - 1:00pm

Machine learning models are being increasingly developed to predict clinical outcomes such as mortality, morbidity, and adverse events. Sophisticated predictive models with excellent performance are reported in the literature at an increasing pace. These models in most cases are regarded as black boxes that produce a prediction for an outcome. However, for such models to be practically useful in clinical care, it is critical to provide clinicians with simple and reliable patient-specific explanations for each prediction. We developed machine learning models to predict severe complications in patients with community-acquired pneumonia (CAP), and evaluated patient-specific explanations using physicians. Our method uses LIME that generates a patient-specific linear model that provides a feature relevance ranking. There was good agreement by physician evaluators on patient-specific explanations that were generated to augment predictions of clinical outcomes. Such explanations can be useful in interpreting predictions of clinical outcomes.

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