4. Risk Stratification, Part 1 | Highlights and Annotations by Gistr.

This lecture covers risk stratification, using machine learning to categorize patients by risk level for targeted interventions. A case study on early type 2 diabetes detection using administrative data (claims, pharmacy, labs) and L1-regularized regression is presented. The lecture emphasizes the importance of aligning with clinical workflows and using appropriate evaluation metrics (e.g., positive predictive value) rather than solely focusing on traditional metrics like AUC. A discussion with a healthcare professional explores real-world applications and challenges in implementing machine learning-based risk stratification. Goals of Risk Stratification Distinguishing Risk Stratification from Diagnosis Risk stratification is a method of categorizing patients into groups based on their risk of experiencing a specific outcome. Traditional vs. Machine Learning Approaches Goal=> Treatment => Prevention, intervention, cost reduction Real-World Examples of Risk Stratification Case Study: Early Detection of Type 2 Diabetes Formulating the Risk Stratification Problem as a Machine Learning Task Challenges with Machine Learning Approaches Evaluation Metrics for Risk Stratification Models Feature Engineering for Type 2 Diabetes Prediction The Role of Cost-Effectiveness in Risk Stratification Addressing Bias and Underdiagnosis in Risk Stratification Challenges in Implementing Risk Stratification in Healthcare Prediction: Identify patients at high risk for a specific outcome. Intervention: Develop and implement strategies to prevent or mitigate the risk. Data Leakage: Information about a patient's condition might be present in the data even if it's not formally coded, leading to biased predictions. Censoring: Missing data can occur due to patients entering or leaving the healthcare system, leading to incomplete information. Interpretability: Machine learning models can be complex, making it difficult to understand the factors driving their predictions. Predicting infant risk of severe morbidity: The Apgar score is a traditional example, but machine learning can improve accuracy. Predicting the need for coronary care unit admission: A 1984 study used logistic regression to identify patients at high risk for heart-related complications. Predicting hospital readmission: This area is gaining attention due to government penalties imposed on hospitals with high readmission rates. Traditional: Scoring systems like the Apgar score are manually calculated and often based on a limited number of features. Machine Learning: Can handle high-dimensional data, integrate into clinical workflows, and are faster to develop. Specialist Visits: Presence or absence of visits to specific specialists. Medications: Presence or absence of specific medications. Laboratory Tests: Whether a test was ever administered and whether the results were low, high, normal, increasing, decreasing, or fluctuating. Problem: Undiagnosed type 2 diabetes is a significant health issue. Goal: Identify patients at risk to implement preventative interventions. Data: Administrative data from health insurance companies, including claims, pharmacy records, and lab tests. Positive Predictive Value (PPV): What fraction of the patients predicted to be high-risk actually develop the outcome? Cost-Effectiveness: Evaluating the impact of interventions based on predictions, considering both cost and benefit. Binary Classification: Predict whether a patient will develop type 2 diabetes within a specific time window. Feature Engineering: Create features from historical data, accounting for missing values and temporal patterns. Algorithm: L1-regularized logistic regression, which encourages sparsity and interpretability. Financial Incentives: Focus on demonstrating the financial benefits of interventions, as this is often a primary driver for healthcare decision-making. Causal Inference: Use methods like propensity matching to estimate the causal impact of interventions. Transparency: Acknowledge the potential for bias and work with stakeholders to ensure fair and equitable outcomes. Data-Driven Approaches: Use data to identify potential biases and inform decisions about data collection and model development. Culture Change: Clinicians may resist using machine learning predictions, preferring their own experience and intuition. Technical Integration: Healthcare systems are often complex, requiring flexible solutions that integrate with existing workflows. Interpretability: Explaining complex models to clinicians and stakeholders can be challenging.