融合预训练语言模型的冠心病专病库建设及应用OA
Construction and Application of Coronary Heart Disease Specialized Knowledge Base by Integrating Pre-trained Language Models
冠心病专病库的数据处理效率和准确性在临床研究与决策中发挥着至关重要的作用.因此,建设一个高效、准确的专病库是十分必要的,可支持临床研究者快速获取关键信息、优化治疗决策,从而提升患者的整体护理质量.基于Clinical-BERT+Bi-LSTM+CRF模型,结合数据平台与企业服务总线(ESB)对专病库数据处理进行优化.实验结果表明,数据抽取时间平均缩短了36倍(t=115.96,P<0.01),结构化数据的准确率提高了6.9％(χ²=222.41,P<0.01),说明这一优化能够有效提升冠心病专病库数据处理的效率和准确性,为冠心病的临床研究和决策提供了可靠的数据支持.
The efficiency and accuracy of data processing in coronary heart disease specialty databases play a crucial role in clinical research and decision-making.Therefore,building a highly efficient and reliable disease-specific database is essential to support clinical researchers in rapidly acquiring key information,optimizing treatment decisions,and improving overall patient care quality.Based on the Clinical-BERT+Bi-LSTM+CRF model and combined with data platform and Enterprise Service Bus(ESB)integration,we optimized the data processing workflow of the specialty database.Experimental results demonstrate that the data extraction time was reduced by an average factor of 36(t=115.96,P<0.01),and the accuracy of structured data increased by 6.9％(χ²=222.41,P<0.01).These findings indicate that the proposed op-timization effectively enhances the efficiency and accuracy of data processing in the coronary heart disease specialty database,providing ro-bust data support for clinical research and decision-making in coronary heart disease.
薛扬;侯旭敏
上海理工大学 健康科学与工程学院,上海 200093上海市胸科医院,上海 200030
信息技术与安全科学
冠心病专病库建设数据处理预训练模型
coronary heart diseasespecialized disease database constructiondata processingpre-trained model
《软件导刊》 2026 (1)
32-38,7
上海市城市数字化转型专项基金项目(202301002)上海市卫生健康委员会智慧医疗专项研究项目(2025ZHYL011)
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