
Amazon SageMaker Clarify enables clinicians to understand and explain machine learning model predictions for hospital triage decisions by using SHAP values to break down how admission notes contribute to mortality risk assessments.


<div><p>This article demonstrates how to use Amazon SageMaker Clarify to explain ML model predictions in clinical settings, specifically for hospital triage and mortality prediction.</p><ul><li>SageMaker Clarify enables explainability of NLP models used in clinical decision support systems</li><li>Uses SHAP (SHapley Additive exPlanations) values to break down model predictions by feature contribution</li><li>Deploys Hugging Face BigBird model fine-tuned on MIMIC ICU admission notes for mortality prediction</li><li>Provides sentence-level explanations color-coded green (supporting prediction) or red (opposing prediction)</li><li>Clinicians can understand reasoning behind individual patient risk assessments for better care decisions</li><li>Includes code examples for deploying model endpoint with Clarify explainer enabled on SageMaker</li></ul><p>This solution integrates explainable AI into clinical workflows, helping healthcare teams make informed triage decisions with transparent model reasoning.</p></div>


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