{
  "abstract": "Background: Identifying diabetes phenotypes in electronic health records is challenging when repeated admissions and diagnosis-derived variables can bias evaluation. Methods: We reconstructed an ICD-audited diabetes phenotype from MIMIC-IV v3.1 and compared LightGBM with trajectory-aware probability stacking and a conservative contextual-bandit reliability gate. Patient-level separation, out-of-fold feature generation, independent gate development, and a common upstream Base model were used. Results: On the primary held-out split (51,400 records), Full SAG-RS achieved accuracy 0.926654, F1 0.889579, AUC 0.961288, and Brier 0.059649. Across five patient-level splits, mean accuracy was 0.928021 ± 0.002217. The gate improved primary-split accuracy over Stack-only by 0.000233 (95% paired confidence interval −0.000078 to 0.000548); net corrections were positive in three of five splits. Conclusions: Trajectory-aware stacking provided most of the gain over admission-level LightGBM. The gate offered limited, variable correction rather than a demonstrated independent performance gain. Findings are restricted to retrospective internal validation; external validation and full-pipeline replication remain outstanding.",
  "abstract_shown": true,
  "affiliations": [
    "College of Information Science and Engineering, Hunan Women’s University, Changsha 410004, China",
    "College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Hongchao Lv"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Yineng Chen",
      "orcid": "0000-0003-2014-7133"
    },
    {
      "affs": [
        0
      ],
      "name": "Qingshan Ren"
    },
    {
      "affs": [
        0
      ],
      "name": "Shukun Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Xianghua Peng"
    },
    {
      "affs": [
        0
      ],
      "name": "Chengkai Chen"
    },
    {
      "affs": [
        0
      ],
      "name": "Yixin Zhang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/d09azgp9yqfme.bib",
  "cited_by": 0,
  "doi": "10.3390/math14213978",
  "fulltext_url": null,
  "id": "d09azgp9yqfme",
  "issns": [
    "2227-7390"
  ],
  "landing_url": "https://www.mdpi.com/2227-7390/14/21/3978",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-01",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/d09azgp9yqfme.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T00:23:28Z",
      "native_id": "10.3390/math14213978",
      "source": "crossref",
      "url": "https://www.mdpi.com/2227-7390/14/21/3978"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "issn:2227-7390",
      "subfield": "general"
    }
  ],
  "title": "SAG-RS: Trajectory-Aware Probability Stacking with Contextual-Bandit Reliability Gating for ICD-Audited Diabetes Phenotype Identification in MIMIC-IV",
  "title_text": "SAG-RS: Trajectory-Aware Probability Stacking with Contextual-Bandit Reliability Gating for ICD-Audited Diabetes Phenotype Identification in MIMIC-IV",
  "type": "peer-reviewed",
  "updated_at": "2026-11-05T00:20:49Z",
  "url": "https://research.draconitic.com/paper/d09azgp9yqfme",
  "venue": "Mathematics",
  "versions": []
}