基于胸外科结构化电子病历的人工智能诊断系统构建实践
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摘要
人工智能技术在临床医疗领域的应用越来越受到重视,主要在对疾病的智能诊断方面,突显其精准迅速,是未来的发展方向。但是,它的实现对电子病历提出了很高的要求。电子病历是目前临床广泛应用的病历形式,越来越向信息化、数字化、智能化方向发展,各临床分科也越来越精细。然而在实际应用过程中,电子病历的版本五花八门,缺乏统一的医学标准术语,缺乏互联互通的整体格局,某医院胸外科参与构建了由中国医师协会牵头的《胸外科医学标准术语字典》,并以此为契机,将胸外科标准化诊疗术语维护进入电子病历系统,进行大量结构化、个性化数据节点设计,并在临床中加以运用,实现医疗资源自动关联,互通共享、依据精准的临床医学统计和大数据分析,实现了人工智能诊断,不仅提高科研能力,减轻医生的工作负担,提升医院信息化程度,更为培训低年资的医生、改进中国基层医疗卫生服务效能等方面提供技术支撑。对某院胸外科基于标准化术语、构建结构化电子病历新模式,尝试人工智能诊断系统的实践,与同仁们共同探讨。
The application of artificial intelligence technology in the field of clinical medicine has been paid more and more attention, mainly in the intelligent diagnosis of diseases, highlighting its accuracy and speed. It is the future direction of development. However, its implementation places high demands on electronic medical records. The electronic medical record is a medical record form widely used in clinical practice, and it is increasingly developing in the direction of informationization, digitization, and intelligence. The clinical divisions are becoming more and more elaborate. However, in the actual application process, the version of the electronic medical record is varied, lacking unified medical standard terminology and the overall pattern of interconnection, and a hospital thoracic surgery participated in the construction of the Dictionary of Standard Terminology of Thoracic Surgery led by the Chinese Medical Doctor Association. Taking this as an opportunity to maintain the standardized diagnosis and treatment term of thoracic surgery into the electronic medical record system, a large number of structured, personalized data node design, and use in the clinical, to achieve automatic correlation of medical resources, mutual sharing, based on accurate clinical medical statistics Big data analysis and artificial intelligence diagnosis have not only improved scientific research capabilities, reduced doctors' workload, improved hospital informationization, but also provided technical support for training low-grade doctors and improving the effectiveness of China's primary health care services. This article discusses the practice of hospital thoracic surgery based on standardized terminology, constructing a new model of structured electronic medical records, and trying artificial intelligence diagnosis systems.
The application of artificial intelligence technology in the field of clinical medicine has been paid more and more attention, mainly in the intelligent diagnosis of diseases, highlighting its accuracy and speed. It is the future direction of development. However, its implementation places high demands on electronic medical records. The electronic medical record is a medical record form widely used in clinical practice, and it is increasingly developing in the direction of informationization, digitization, and intelligence. The clinical divisions are becoming more and more elaborate. However, in the actual application process, the version of the electronic medical record is varied, lacking unified medical standard terminology and the overall pattern of interconnection, and a hospital thoracic surgery participated in the construction of the Dictionary of Standard Terminology of Thoracic Surgery led by the Chinese Medical Doctor Association. Taking this as an opportunity to maintain the standardized diagnosis and treatment term of thoracic surgery into the electronic medical record system, a large number of structured, personalized data node design, and use in the clinical, to achieve automatic correlation of medical resources, mutual sharing, based on accurate clinical medical statistics Big data analysis and artificial intelligence diagnosis have not only improved scientific research capabilities, reduced doctors' workload, improved hospital informationization, but also provided technical support for training low-grade doctors and improving the effectiveness of China's primary health care services. This article discusses the practice of hospital thoracic surgery based on standardized terminology, constructing a new model of structured electronic medical records, and trying artificial intelligence diagnosis systems.
引文
[1]尹海华.视诊通CT肺结节辅助诊断系统[N].中国科学报,2017-07-27.
[2]梅文华,刁君,常奕,等.结构化电子病历的应用[J].中国数字医学,2016,11(3):22-25.
[3]郑西川,胡燕峰,冯贞贞,等.结构化描述语言电子病历模型及其关键技术实现[J].中国数字医学,2011,6(5):40-43.
[4]赵嘉莹,高鹏,朱勇俊,等.人工智能的应用将改进中国基层医疗卫生服务效能[J].中国全科医学,2017,20(34):219-223.
[5]姜杰,于修义.基于标准化术语的胸外科结构化电子病历实践[J].中国肺癌杂志,2018(4):256-259.
[2]梅文华,刁君,常奕,等.结构化电子病历的应用[J].中国数字医学,2016,11(3):22-25.
[3]郑西川,胡燕峰,冯贞贞,等.结构化描述语言电子病历模型及其关键技术实现[J].中国数字医学,2011,6(5):40-43.
[4]赵嘉莹,高鹏,朱勇俊,等.人工智能的应用将改进中国基层医疗卫生服务效能[J].中国全科医学,2017,20(34):219-223.
[5]姜杰,于修义.基于标准化术语的胸外科结构化电子病历实践[J].中国肺癌杂志,2018(4):256-259.