{
  "abstract": "Class imbalance and limited minority-class samples remain major challenges for developing reliable clinical diagnostic models, as insufficient observations often fail to capture complex minority-class distributions. Existing augmentation methods either rely on heuristic interpolation or suffer from instability when learning sparse and heterogeneous medical tabular data. This study proposes IFC-HFlowVAE, an iterative feedback and consensus framework built upon a flow-enhanced heterogeneous variational autoencoder for minority-class data generation. The proposed framework first models mixed-type clinical attributes through HFlowVAE and then introduces a self-circulating refinement strategy to progressively improve generated samples. To alleviate degradation during iterative refinement, SMOTE-generated samples are incorporated as structural references, followed by a localized GMM-based refinement procedure that guides samples toward more representative minority-class regions. Comprehensive experiments on seven clinical tabular datasets demonstrate that IFC-HFlowVAE improves downstream classification performance compared with existing augmentation approaches. Furthermore, fidelity analyses show that the proposed framework better preserves challenging minority-class characteristics, particularly skewed and multimodal numerical distributions.",
  "abstract_shown": true,
  "affiliations": [
    "School of Big Data & Software Engineering, Chongqing University, Chongqing, China"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Lu Yuwen"
    },
    {
      "name": "Shuyu Chen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bwhegdhkhptbq.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0357260",
  "fulltext_url": null,
  "id": "bwhegdhkhptbq",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0357260",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1702|Imbalanced Data Classification Techniques"
    ],
    [
      "plos",
      "/Biology and life sciences/Anatomy/Endocrine system/Thyroid"
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      "/Computer and information sciences/Software engineering/Preprocessing"
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      "plos",
      "/Engineering and technology/Software engineering/Preprocessing"
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      "plos",
      "/Medicine and health sciences/Anatomy/Endocrine system/Thyroid"
    ],
    [
      "plos",
      "/Medicine and health sciences/Endocrinology/Endocrine disorders/Diabetes mellitus"
    ],
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      "plos",
      "/Medicine and health sciences/Medical conditions/Metabolic disorders/Diabetes mellitus"
    ],
    [
      "plos",
      "/Physical sciences/Mathematics/Numerical analysis/Interpolation"
    ],
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      "plos",
      "/Physical sciences/Mathematics/Probability theory/Probability distribution/Skewness"
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      "plos",
      "/Physical sciences/Mathematics/Probability theory/Statistical distributions"
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  "openalex": "W7207564413",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0357260&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-03",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/bwhegdhkhptbq.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:56Z",
      "native_id": "10.1371/journal.pone.0357260",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0357260"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "plos:probability theory",
      "subfield": "probability"
    },
    {
      "field": "medicine",
      "origin": "plos:endocrinology",
      "subfield": "endocrinology-metabolism"
    },
    {
      "field": "medicine",
      "origin": "plos:anatomy",
      "subfield": "physiology"
    }
  ],
  "title": "IFC-HFlowVAE: A self-enhancing generative framework with structural anchoring for imbalanced clinical data augmentation",
  "title_text": "IFC-HFlowVAE: A self-enhancing generative framework with structural anchoring for imbalanced clinical data augmentation",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:56Z",
  "url": "https://research.draconitic.com/paper/bwhegdhkhptbq",
  "venue": "PLOS One",
  "versions": []
}