{
  "abstract": "ObjectiveTo develop and internally validate an interpretable machine-learning model for traditional Chinese medicine (TCM) syndrome differentiation in ischemic stroke by integrating modern clinical characteristics with TCM-related features.MethodsA total of 616 patients with ischemic stroke from a tertiary TCM hospital in Sichuan Province, China, were included. Fifty-one candidate predictors covered demographics, stroke severity, laboratory indices, infarction characteristics, medical history, TCM symptoms, tongue features, and pulse characteristics. Five TCM syndrome classes were considered. Patients were stratified into a training set (n = 495) and a held-out test set (n = 121). Grouped multinomial least absolute shrinkage and selection operator regression was used for feature selection. Random forest (RF), support vector machine with a radial basis function kernel (SVM-RBF), and extreme gradient boosting (XGBoost) were trained using identical stratified 10-fold cross-validation partitions. Performance was assessed using accuracy, Cohen’s κ, Macro-F1, macro balanced accuracy, Macro AUC, and Hand-Till multiclass AUC, with 95% confidence intervals estimated by 2,000 bootstrap resamples. Model calibration and TreeSHAP-based interpretability were further evaluated for the final model.ResultsAt λ1se = 0.0281, 33 encoded features were retained. In the held-out test set, RF achieved the highest accuracy (0.752; 95% CI, 0.678–0.826) and Macro-F1 (0.744; 0.650–0.819), whereas XGBoost achieved the highest Macro AUC (0.945; 0.916–0.970) and Hand-Till multiclass AUC (0.944; 0.912–0.970). Overall classification correctness did not differ significantly among the three models (Q = 4.065, P = 0.131), and paired RF–XGBoost bootstrap differences showed no clear overall performance advantage. XGBoost was therefore selected for further analysis. Its multiclass Brier score was 0.358 (0.264–0.463), with a Brier skill score of 0.540. SHAP identified LDL-C, age, wiry pulse, BMI, waist and knee soreness, red tongue, enlarged tongue, homocysteine, triglycerides, and hypertension as leading global contributors, with distinct class-specific attribution patterns across syndromes.ConclusionXGBoost showed promising discrimination in the single-center internal test set; however, classification performance and calibration varied across syndrome classes. These findings represent a preliminary internal evaluation and do not establish clinical readiness or support the use of the model for clinical decision-making or prescription guidance. External validation and prospective evaluation are required before clinical implementation.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Original Research",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "Renlong Liang"
    },
    {
      "name": "Heng Ding"
    },
    {
      "name": "Rui Jian"
    },
    {
      "name": "Yingxin Jia"
    },
    {
      "name": "Min Lin"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0zfhhkkqdn1xp.bib",
  "cited_by": 0,
  "doi": "10.3389/fphar.2026.1983993",
  "fulltext_url": null,
  "id": "0zfhhkkqdn1xp",
  "issns": [
    "1663-9812"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fphar.2026.1983993",
  "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": [
    [
      "feed-journal",
      "medicine/pharmacology-toxicology"
    ]
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  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-12",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/0zfhhkkqdn1xp.ris",
  "sources": [
    {
      "first_seen": "2026-11-12T12:20:59Z",
      "native_id": "10.3389/fphar.2026.1983993",
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      "url": "https://www.frontiersin.org/articles/10.3389/fphar.2026.1983993"
    },
    {
      "first_seen": "2026-11-12T12:23:19Z",
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      "url": "https://www.frontiersin.org/articles/10.3389/fphar.2026.1983993/full"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "feed-journal:medicine/pharmacology-toxicology",
      "subfield": "pharmacology-toxicology"
    }
  ],
  "title": "An interpretable machine-learning model for Traditional Chinese Medicine syndrome differentiation in ischemic stroke using integrated disease–syndrome features",
  "title_text": "An interpretable machine-learning model for Traditional Chinese Medicine syndrome differentiation in ischemic stroke using integrated disease–syndrome features",
  "type": "peer-reviewed",
  "updated_at": "2026-11-14T00:21:07Z",
  "url": "https://research.draconitic.com/paper/0zfhhkkqdn1xp",
  "venue": "Frontiers in Pharmacology",
  "versions": []
}