使用具有专家知识增强提示的大型语言模型对静脉血栓栓塞进行自动风险评分：一项多中心验证研究
Automated risk scoring for venous thromboembolism using large language models with expert knowledge-augmented prompting: a multicenter validation study.
摘要(中文译文)
静脉血栓栓塞症（VTE）是住院患者常见但可预防的并发症，然而利用非结构化电子健康记录（EHR）进行标准化风险评分仍具有挑战性。我们回顾性分析了来自全国30家医院多中心队列的匿名化EHR。采用分层随机抽样，从10家医院选取50例用于开发，从其余20家医院选取200例用于测试。通过涉及19名专家的德尔菲流程，开发了针对Padua和Caprini评分的专家知识增强提示词，并与基础提示词和复杂提示词进行比较。使用六种开源大语言模型（LLM）评估性能，以患病率校正偏倚校正kappa（PABAK）和F1分数作为主要指标。专家知识增强提示词在两个数据集中均表现出最高性能。在测试数据集中，Padua的平均条目级PABAK和F1分数分别为0.97和0.96，Caprini分别为0.97和0.93；大多数条目的PABAK和F1分数均高于0.90。Padua的风险分层效果（PABAK 0.92；F1分数0.96）优于Caprini（PABAK 0.73；F1分数0.64），每例处理时间分别为6.07秒和12.82秒。这些发现表明，采用专家知识增强提示词的LLM可有效支持血栓预防工作流程中Padua和Caprini评分的自动化评分及风险分层。
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.
如何引用
AMA
Jing Ma, Dingyi Wang, Yaqian Zhang, Zhaofei Chen, Kaiyuan Zhen, Mengyao Li, et al. Automated risk scoring for venous thromboembolism using large language models with expert knowledge-augmented prompting: a multicenter validation study.. NPJ digital medicine. 2026; doi:10.1038/s41746-026-02929-3.
APA
Jing Ma, Dingyi Wang, Yaqian Zhang, Zhaofei Chen, Kaiyuan Zhen, Mengyao Li, et al (2026). Automated risk scoring for venous thromboembolism using large language models with expert knowledge-augmented prompting: a multicenter validation study.. NPJ digital medicine. https://doi.org/10.1038/s41746-026-02929-3
GB/T 7714
Jing Ma, Dingyi Wang, Yaqian Zhang, Zhaofei Chen, Kaiyuan Zhen, Mengyao Li, et al. Automated risk scoring for venous thromboembolism using large language models with expert knowledge-augmented prompting: a multicenter validation study.[J]. NPJ digital medicine, 2026 doi:10.1038/s41746-026-02929-3.
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