{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.36839",
  "author_total": 4,
  "authors": [
    {
      "name": "Yulong Li"
    },
    {
      "name": "Ke Wan"
    },
    {
      "name": "Toshio Shimokawa"
    },
    {
      "name": "Kensuke Tanioka"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2d1x2wbfcm39e.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.36839",
  "fulltext_url": null,
  "id": "2d1x2wbfcm39e",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.36839",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "openalex",
      "2613|Statistical Methods and Inference"
    ]
  ],
  "openalex": "W7214985615",
  "pdf_url": "https://arxiv.org/pdf/2609.36839",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/2d1x2wbfcm39e.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:41Z",
      "native_id": "2609.36839",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.36839"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "BRP-GABLE: An Interpretable Rule-Based Prediction Framework for Multiple Outcome Types",
  "title_text": "BRP-GABLE: An Interpretable Rule-Based Prediction Framework for Multiple Outcome Types",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:41Z",
  "url": "https://research.draconitic.com/paper/2d1x2wbfcm39e",
  "venue": "arXiv",
  "versions": []
}