CaliForest

CaliForest implements calibrated random forest models to improve probability calibration while preserving discrimination for binary risk prediction tasks in healthcare.

Key Features:

• Calibrated Random Forest Algorithm: Extends the random forest algorithm with an integrated calibration procedure to improve the accuracy of predicted risk probabilities.
• Utilization of Out-of-Bag Samples: Uses out-of-bag (OOB) samples from random forest training to perform calibration without requiring a separate calibration dataset.
• Evaluation on Healthcare Data: Validated on two binary risk prediction tasks derived from the MIMIC-III clinical database.
• Comprehensive Calibration Metrics: Assesses calibration performance across six distinct metrics in addition to discrimination.

Scientific Applications:

• Clinical risk prediction: Produces calibrated probability estimates for patient-level binary risk assessments in healthcare datasets such as MIMIC-III.
• Decision support and personalized treatment planning: Supplies calibrated risk estimates to inform clinical decision-making and individualized treatment strategies.
• Model evaluation and benchmarking: Enables comparison of calibration and discrimination trade-offs in predictive model development for domains requiring reliable risk prediction.

Methodology:

The method integrates random forest algorithms with a calibration technique that uses out-of-bag (OOB) samples and assesses performance using six calibration metrics on two binary risk prediction tasks from MIMIC-III.