{
  "abstract": "Venous thromboembolism (VTE) is a common but preventable complication in hospitalized patients, yet standardized risk scoring using unstructured electronic health records (EHRs) remains challenging. We retrospectively analyzed anonymized EHRs from a nationwide multicenter cohort across 30 hospitals. Using stratified random sampling, 50 cases from 10 hospitals were used for development and 200 cases from the remaining 20 hospitals for testing. Expert knowledge-augmented prompts for the Padua and Caprini scores were developed through a Delphi process involving 19 specialists and compared with basic and complex prompts. Performance was evaluated using six open-source large language models (LLMs), with prevalence-adjusted bias-adjusted kappa (PABAK) and F1 score as primary metrics. Expert knowledge-augmented prompts showed the highest performance in both datasets. In the test dataset, mean item-level PABAK and F1 scores were 0.97 and 0.96 for Padua, and 0.97 and 0.93 for Caprini; most items showed PABAK and F1 scores above 0.90. Risk stratification was better for Padua (PABAK 0.92; F1 score 0.96) than for Caprini (PABAK 0.73; F1 score 0.64), with processing times of 6.07 s and 12.82 s per case, respectively. These findings indicated that LLMs with expert knowledge-augmented prompting could efficiently support automated Padua and Caprini scoring and risk stratification in thrombosis prevention workflows.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, China",
    "National Center for Respiratory Medicine; State Key Laboratory of Respiratory Health and Multimorbidity; National Clinical Research Center for Respiratory Diseases; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College; Department of Clinical research and Data management, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing, China",
    "Department of Respiratory and Critical Care Medicine, China-Japan Friendship Institute of Clinical Medicine, Beijing, China",
    "China-Japan Friendship Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China",
    "Respiratory Department, Center for Pulmonary Vascular Diseases, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases, Beijing, China",
    "Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China",
    "National Center for Respiratory Medicine; State Key Laboratory of Respiratory Health and Multimorbidity; National Clinical Research Center for Respiratory Diseases; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College; Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing, China",
    "Capital Medical University, Beijing, China",
    "Peking University China-Japan Friendship School of Clinical Medicine; Department of Pulmonary and Critical Care Medicine; Center of Respiratory Medicine; National Center for Respiratory Medicine; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences; National Clinical Research Center for Respiratory Diseases, China-Japan Friendship Hospital, Beijing, China",
    "Beijing University of Chinese Medicine, Beijing, China",
    "China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China",
    "BioMap Co., Ltd, Beijing, China",
    "Zhejiang University, Hangzhou, China",
    "Department of Physiology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China",
    "Department of Pulmonary and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China",
    "Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China",
    "Department of Pulmonary Vessel and Thrombotic Disease, Sixth Medical Center, Chinese PLA General Hospital, Beijing, China",
    "Medical Affairs Department of China-Japan Friendship Hospital, Beijing, China",
    "Department of Nursing, China-Japan Friendship Hospital, Beijing, China",
    "Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China",
    "China-Japan Friendship Hospital, Beijing, China",
    "Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China",
    "School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College; State Key Laboratory of Respiratory Health and Multimorbidity; Key Laboratory of Pathogen Infection Prevention and Control (Peking Union Medical College), Ministry of Education, Beijing, China"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 29,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Jing Ma"
    },
    {
      "affs": [
        1
      ],
      "name": "Dingyi Wang"
    },
    {
      "affs": [
        2
      ],
      "name": "Yaqian Zhang"
    },
    {
      "affs": [
        3
      ],
      "name": "Zhaofei Chen"
    },
    {
      "affs": [
        4
      ],
      "name": "Kaiyuan Zhen"
    },
    {
      "affs": [
        5,
        6
      ],
      "name": "Mengyao Li"
    },
    {
      "affs": [
        5,
        6
      ],
      "name": "Jingrui Li"
    },
    {
      "affs": [
        7
      ],
      "name": "Yunfan Shi"
    },
    {
      "affs": [
        8
      ],
      "name": "Xiaomeng Zhang"
    },
    {
      "affs": [
        9
      ],
      "name": "Rui Liang"
    },
    {
      "affs": [
        10
      ],
      "name": "Feiya Xu"
    },
    {
      "affs": [
        11
      ],
      "name": "Siqi Ma"
    },
    {
      "affs": [
        12
      ],
      "name": "Jintao Chen"
    },
    {
      "affs": [
        13
      ],
      "name": "Yanshuang Lyu"
    },
    {
      "affs": [
        14
      ],
      "name": "Yuanhua Yang"
    },
    {
      "affs": [
        15
      ],
      "name": "Zhe Cheng"
    },
    {
      "affs": [
        16
      ],
      "name": "Yutao Guo"
    },
    {
      "affs": [
        17
      ],
      "name": "Lei Xia"
    },
    {
      "affs": [
        18
      ],
      "name": "Wei Wang"
    },
    {
      "affs": [
        19
      ],
      "name": "Bing Liu"
    },
    {
      "affs": [
        20
      ],
      "name": "Cunbo Jia"
    },
    {
      "affs": [
        6
      ],
      "name": "Shuai Zhang"
    },
    {
      "affs": [
        6
      ],
      "name": "Qian Gao"
    },
    {
      "affs": [
        6
      ],
      "name": "Yunxia Zhang"
    },
    {
      "affs": [
        6
      ],
      "name": "Zhu Zhang"
    },
    {
      "affs": [
        6
      ],
      "name": "Wanmu Xie"
    },
    {
      "affs": [
        1
      ],
      "name": "Guohui Fan"
    },
    {
      "affs": [
        19,
        21
      ],
      "name": "Shengfeng Wang"
    },
    {
      "affs": [
        6,
        22
      ],
      "name": "Zhenguo Zhai",
      "orcid": "0000-0002-7096-8792"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/c2f0rzjdymfdw.bib",
  "cited_by": 0,
  "doi": "10.1038/s41746-026-02929-3",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13728113/",
  "id": "c2f0rzjdymfdw",
  "issns": [
    "2398-6352"
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  "landing_url": "https://doi.org/10.1038/s41746-026-02929-3",
  "language": "en",
  "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,
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      "1702|Machine Learning in Healthcare"
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  "pdf_url": "https://www.nature.com/articles/s41746-026-02929-3_reference.pdf",
  "pmcid": "PMC13728113",
  "pmid": "42426240",
  "published": "2026-07-09",
  "publisher": "Nature Publishing Group",
  "ris": "https://research.draconitic.com/paper/c2f0rzjdymfdw.ris",
  "sources": [
    {
      "first_seen": "2026-11-06T12:23:51Z",
      "native_id": "PMC13728113",
      "source": "pmc",
      "url": "https://doi.org/10.1038/s41746-026-02929-3"
    }
  ],
  "subjects": [],
  "title": "Automated risk scoring for venous thromboembolism using large language models with expert knowledge-augmented prompting: a multicenter validation study",
  "title_text": "Automated risk scoring for venous thromboembolism using large language models with expert knowledge-augmented prompting: a multicenter validation study",
  "type": "peer-reviewed",
  "updated_at": "2026-11-07T12:21:10Z",
  "url": "https://research.draconitic.com/paper/c2f0rzjdymfdw",
  "venue": "NPJ Digital Medicine",
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}