Explainability of machine learning (ML) models used in the medical domain is becoming increasingly important because models need to be explained from a number of perspectives in order to gain adoption. These perspectives range from medical, technological, legal, and the most important perspective—the patient’s. Models developed on text in the medical domain have become accurate statistically, yet clinicians are ethically required to evaluate areas of weakness related to these predictions in order to provide the best care for individual patients. Explainability of these predictions is required in order for clinicians to make the correct choices on a patient-by-patient basis.
In this post, we show how to improve model explainability in clinical settings using Amazon SageMaker Clarify.
Background
One specific application of ML algorithms in the medical domain, which uses large volumes of text, is clinical decision support systems (CDSSs) for triage. On a daily basis, patients are admitted to hospitals

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