{
  "abstract": "Objective: This study aimed to develop and externally validate a machine learning-based risk prediction model of ischemia and bleeding events in patients receiving percutaneous coronary intervention (PCI) and dual antiplatelet therapy (DAPT) and, to evaluate its clinical potential and economic implications compared with existing risk scoring systems. Methods: A weighted LightGBM model was trained on a PCI cohort from the United Arab Emirates, comprising 4,812 participants, and then externally validated on the MIMICIV database (3,406 patients). The main outcomes were composite ischemic events and major bleeding events. The model discrimination, calibration, and clinical utility were evaluated using calibration plot, AUROC, and decision curve analysis. This model was used to estimate economic results under hypothetical risk-guided management strategies. Results: The weighted LightGBM model achieved AUROC values of 0.87 for ischemic events and 0.85 for bleeding events during internal validation. In external validation, AUROC values were 0.84 and 0.82, respectively. These results were higher than discrimination performance of the established scoring systems, including DAPT and PRECISE-DAPT. Exploratory analyses suggested that risk-guided strategies informed by the model may have potential, although these findings require prospective validation. Conclusions: The explainable AI model demonstrated good discrimination and calibration for post-PCI ischemic and bleeding risk prediction. The model showed higher predictive performance compared with conventional risk scores and may serve as a potential decision-support tool to inform future prospective validation evaluating its clinical utility.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Internal Medicine (Cardiology), Tongji Medical College, Huazhong University of Science and Technology, Hubei, Wuhan, China"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Hassa Iftikhar"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/821pee3yxzzsh.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0353066",
  "fulltext_url": null,
  "id": "821pee3yxzzsh",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0353066",
  "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",
      "2705|Antiplatelet Therapy and Cardiovascular Diseases"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning"
    ],
    [
      "plos",
      "/Medicine and health sciences/Cardiology/Cardiovascular medicine/Cardiovascular diseases/Cardiovascular disease risk"
    ],
    [
      "plos",
      "/Medicine and health sciences/Clinical medicine/Signs and symptoms/Hemorrhage"
    ],
    [
      "plos",
      "/Medicine and health sciences/Epidemiology/Medical risk factors"
    ],
    [
      "plos",
      "/Medicine and health sciences/Medical conditions/Cardiovascular diseases/Cardiovascular disease risk"
    ],
    [
      "plos",
      "/Medicine and health sciences/Pharmaceutics/Drug therapy/Antiplatelet therapy"
    ],
    [
      "plos",
      "/Medicine and health sciences/Vascular medicine/Hemorrhage"
    ],
    [
      "plos",
      "/Physical sciences/Mathematics/Statistics/Statistical methods/Forecasting"
    ],
    [
      "plos",
      "/Research and analysis methods/Mathematical and statistical techniques/Statistical methods/Forecasting"
    ],
    [
      "plos",
      "/Social sciences/Economics/Economic analysis/Cost-effectiveness analysis"
    ]
  ],
  "openalex": "W7213368973",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0353066&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-15",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/821pee3yxzzsh.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:49Z",
      "native_id": "10.1371/journal.pone.0353066",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0353066"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "plos:statistical methods",
      "subfield": "statistics"
    },
    {
      "field": "medicine",
      "origin": "plos:cardiovascular diseases",
      "subfield": "cardiovascular"
    },
    {
      "field": "medicine",
      "origin": "plos:epidemiology",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "A machine learning model for predicting ischemic and bleeding risk after percutaneous coronary intervention: Development and external validation",
  "title_text": "A machine learning model for predicting ischemic and bleeding risk after percutaneous coronary intervention: Development and external validation",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:49Z",
  "url": "https://research.draconitic.com/paper/821pee3yxzzsh",
  "venue": "PLOS One",
  "versions": []
}