Prediction in claims databases for epidemiological research

Machine learning and prediction modeling in healthcare claims databases face unique challenges—including right censoring, feature engineering for longitudinal data, and index date selection—that leave many applied researchers without practical guidance.

Target RWE researchers presented a framework for prediction in healthcare claims databases covering index date selection, longitudinal feature engineering strategies, model training with right-censored data, and the potential of deep learning for sequential healthcare event data.

Providing an applied prediction modeling framework for claims databases builds Target RWE's methodological differentiation in machine learning-based patient identification and risk stratification.

Claims-based prediction modeling framework for patient identification and risk stratification in pharma programs, Machine learning methodology guidance for right-censored healthcare data in RWE studies