BRP-GABLE: An Interpretable Rule-Based Prediction Framework for Multiple Outcome Types
Abstract
Interpretable prediction models are important in biomedical research, where predictive accuracy must often be balanced against the ability to examine predictor-outcome relationships. Automatic Binary Logistic Estimation (ABLE) provides interpretable rule-based representations by constructing additive models using threshold rules. However, the original method is restricted to binary outcomes, and its greedy forward construction follows a search path, making candidate rules dependent on earlier selections and potentially excluding alternative structures. We extended ABLE to binary, continuous, and time-to-event outcomes using outcome-specific fitting criteria, yielding Generalized ABLE (GABLE). We then propose bootstrap rule pooling for GABLE (BRP-GABLE), which applies GABLE rule generation to bootstrap samples to explore alternative search paths, pool distinct rules, and perform a single global LASSO selection over the pooled rules and truncated linear terms. Unlike conventional bagging, bootstrap-specific models are not averaged, and resampling is used to diversify the candidate rule space before constructing a final model. Simulations showed that BRP-GABLE consistently improved predictive performance compared with GABLE and provided competitive prediction with relatively simple rule structures. Application to overall survival demonstrated its use for clinically interpretable prediction. BRP-GABLE provides a flexible framework for constructing sparse and interpretable predictive models across multiple outcome types.
Related
- One-step Outcome Imputation: An Alternative to Multiple Imputation
- What Comes Next and Why: Interpretable Next-Event Prediction with Neuro-Symbolic Rules
- Gradient boosting machine for survival prediction in studies with general failure-time outcome-dependent sampling design.
- Fragment-Level Graph Encoding for Interpretable Molecular Property Prediction
- Food-System Stress Testing: Prediction Tasks Under Alternative Outcome Definitions
BibTeX
@misc{li2026brpgable,
title = {{BRP-GABLE: An Interpretable Rule-Based Prediction Framework for Multiple Outcome Types}},
author = {Yulong Li and Ke Wan and Toshio Shimokawa and Kensuke Tanioka},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.36839},
eprint = {2609.36839},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.36839},
}
RIS
TY - UNPB
TI - BRP-GABLE: An Interpretable Rule-Based Prediction Framework for Multiple Outcome Types
AU - Yulong Li
AU - Ke Wan
AU - Toshio Shimokawa
AU - Kensuke Tanioka
PY - 2026
DA - 2026/09/29
PB - arXiv
DO - 10.48550/arxiv.2609.36839
UR - https://arxiv.org/abs/2609.36839
AB - Interpretable prediction models are important in biomedical research, where predictive accuracy must often be balanced against the ability to examine predictor-outcome relationships. Automatic Binary Logistic Estimation (ABLE) provides interpretable rule-based representations by constructing additive models using threshold rules. However, the original method is restricted to binary outcomes, and its greedy forward construction follows a search path, making candidate rules dependent on earlier selections and potentially excluding alternative structures. We extended ABLE to binary, continuous, and time-to-event outcomes using outcome-specific fitting criteria, yielding Generalized ABLE (GABLE). We then propose bootstrap rule pooling for GABLE (BRP-GABLE), which applies GABLE rule generation to bootstrap samples to explore alternative search paths, pool distinct rules, and perform a single global LASSO selection over the pooled rules and truncated linear terms. Unlike conventional bagging, bootstrap-specific models are not averaged, and resampling is used to diversify the candidate rule space before constructing a final model. Simulations showed that BRP-GABLE consistently improved predictive performance compared with GABLE and provided competitive prediction with relatively simple rule structures. Application to overall survival demonstrated its use for clinically interpretable prediction. BRP-GABLE provides a flexible framework for constructing sparse and interpretable predictive models across multiple outcome types.
ER -