Leveraging Interpretable Machine Learning for Granular Risk Stratification in Hospital Readmission: Unveiling Actionable Insights from Electronic Health Records

Abstract

Methodology:

Data Acquisition and Preprocessing:

We access de-identified EHR data from a large healthcare system encompassing a diverse patient population. The data encompasses a comprehensive range of clinical information, including:

• Demographics: Age, gender, ethnicity, socioeconomic status indicators (if available)
• Diagnoses: Recorded using International Classification of Diseases (ICD) codes
• Medications: Prescribed medications and dosages during the hospitalization and any prior prescriptions documented in the EHR
• Procedures: Performed during the index hospitalization and any relevant past procedures
• Laboratory Results: Blood tests, imaging studies, and other relevant laboratory investigations

Following data acquisition, a rigorous cleaning and pre-processing stage is undertaken. This includes handling missing values through imputation techniques (e.g., mean/median imputation, forward fill), identifying and correcting outliers, and transforming categorical variables into a suitable format for machine learning algorithms. Feature engineering techniques are then applied to create additional features that may enhance model performance. This might involve deriving new variables based on existing ones, such as Charlson Comorbidity Index (CCI) score to capture overall patient comorbidity burden.

Model Development and Interpretability:

Our study explores a multifaceted approach to interpretable machine learning for readmission risk prediction. We leverage a combination of interpretable algorithms and techniques:

• Rule-based models: These models express decision-making logic in a human-readable format (e.g., "if a patient has congestive heart failure (CHF) and a prior hospitalization for pneumonia in the past 6 months, then they are classified as high risk"). While offering high interpretability, rule-based models can be less flexible for complex datasets.
• Decision Trees: These tree-like structures represent classification rules by progressively splitting the data based on specific features. Decision trees provide a clear visualization of the decision-making hierarchy, allowing clinicians to understand the sequence of factors leading to a particular risk classification.
• Local Interpretable Model-Agnostic Explanations (LIME): This technique generates explanations for individual patient predictions from any black-box model. LIME works by approximating the model's behavior locally around a specific data point, highlighting the most influential features contributing to the prediction for that particular patient.

By utilizing this combination of interpretable algorithms, we aim to achieve a balance between model accuracy and the ability to explain risk predictions in a clinically meaningful way.

Model Evaluation:

We employ a standard approach to model evaluation, encompassing metrics that assess both prediction performance and calibration. Common metrics used include:

• Area Under the Receiver Operating Characteristic Curve (AUROC): This metric summarizes a model's ability to discriminate between patients who will and will not be readmitted. A higher AUROC value indicates better discriminative ability.
• Sensitivity: This metric represents the proportion of true positives (patients correctly classified as high risk who are subsequently readmitted)
• Specificity: This metric represents the proportion of true negatives (patients correctly classified as low risk who are not readmitted)
• Positive Predictive Value (PPV): This metric indicates the probability that a patient predicted as high risk will actually be readmitted.

To ensure robust evaluation, we employ techniques such as k-fold cross-validation to mitigate overfitting and provide a more generalizable estimate of model performance.

Key Findings:

Our study yields promising results, demonstrating the effectiveness of IML in building accurate and interpretable readmission risk prediction models. The developed model achieves an AUROC of [insert value], indicating good discriminative ability in identifying patients at high risk of hospital readmission. Importantly, the interpretability of the model is achieved through a two-pronged approach:

1. Feature Importance Scores: By analyzing the weights assigned to each feature by the model, we identify the most significant factors contributing to readmission risk. These might include factors such as a history of specific chronic diseases, specific medication use during hospitalization, or abnormal laboratory values.
2. LIME Explanations: For individual patient predictions, LIME generates explanations highlighting the most relevant EHR elements influencing their predicted risk. This allows clinicians to delve deeper into the rationale behind a specific risk classification for a particular patient. For instance, LIME might reveal that a patient's high predicted risk is driven by a combination of factors, such as a recent diagnosis of pneumonia, presence of multiple chronic conditions, and evidence of functional limitations documented in the nursing notes.

These interpretable insights empower clinicians to not only identify high-risk patients but also understand the specific risk factors driving their readmission vulnerability. This knowledge can inform targeted interventions aimed at mitigating these risk factors and potentially reducing readmission rates.