Leveraging Interpretable Machine Learning for Granular Risk Stratification in Hospital Readmission: Unveiling Actionable Insights from Electronic Health Records
Keywords:
Hospital Readmission, Machine Learning, Interpretable Machine LearningAbstract
We acquire de-identified EHR data from a major hospital with many patients. Provide complete clinical data, including age, gender, ethnicity, and SES (if available). Coded ICD diagnosis. EHR and hospital doses Hospitalization history and procedures Blood, imaging, etc. lab findings. Data is cleaned and prepared after collection. Machine learning recovers missing categorical values via mean/median imputation, forward fill, and outlier detection and correction. Adding features boosts model performance. Charlson Comorbidity Index (CCI) values may also reflect patient comorbidity.
We study readmission risk prediction using multidimensional interpretable machine learning. Rule-based models utilize human-readable logic, such as "high risk" for CHF patients with a past pneumonia hospitalization within 6 months. Rules-based models examine complicated data but are less adaptable.
Using attributes, decision trees classify data. Clinicians may rate risk via decision trees.
From any black-box model, LIME explains patient predictions. Approximating the model's behavior around a data point emphasizes the patient's forecast's strengths.
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