围手术期大数据的应用与展望

易斌; 李洪

doi:10.16016/j.2097-0927.202302091

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围手术期大数据的应用与展望

专题报道：围术期大数据治理与AI算法研究 | 更新时间：2026-01-09

- 围手术期大数据的应用与展望
- Application and prospect of big data during perioperative period
- 陆军军医大学学报 2023年45卷第8期页码：725-731
- 作者机构：
  
  1.400038 重庆, 陆军军医大学(第三军医大学)第一附属医院麻醉科
  2.400037 重庆, 陆军军医大学(第三军医大学)第二附属医院麻醉科
- 作者简介：
  
  [易斌，教授，主任医师，博士研究生导师，陆军军医大学第一附属医院麻醉科主任。中华医学会麻醉学分会委员、全军麻醉青年委员会副主任委员、重庆市医学会麻醉学分会副主任委员、重庆市医师会麻醉学分会常委。陆军科技英才、重庆英才·创新领军人才，陆军军医大学优秀研究生导师。主持国家自然科学基金项目5项，国家重点研发计划、国家科技支撑计划分课题各1项，重庆市教改重点课题1项，中国学位与研究生教育委员会等教学课题4项，重庆市课题4项。第一/通讯作者发表SCI 40篇，其中TOP 6篇，最高影响因子17.694。获重庆市科技进步一等奖2项、军队医疗成果奖二等奖1项。参加了“北京小汤山抗击非典”“汶川抗震救灾”“2020年武汉抗疫”“2022年上海抗疫”等，荣立三等功2次。E-mail: yibin1974@163.com YI Bin, E-mail: yibin1974@163.com
  [李洪，教授，主任医师，博士研究生导师，陆军军医大学第二附属医院麻醉科主任。德国柏林心脏中心访问学者，美国弗吉尼亚大学博士后，国家重点研发计划首席科学家。获得国之名医·优秀风范、重庆英才、创新领军人才、重庆市引进高层次人才等荣誉称号。中华医学会麻醉学分会常务委员、重庆市医学会麻醉学分会主任委员、中国医学装备协会麻醉学分会副主任委员、中华医学会麻醉学分会麻醉与人工智能学组副组长。主要从事麻醉与围手术期器官保护、脑功能与人工智能等研究，主持国家重点研发计划、国家自然科学基金等省部级以上课题15项；获国家发明专利、实用新型专利授权7项。获军队医疗成果一等奖、三等奖各1项，荣立三等功1次。E-mail: lh78553@126.com LI Hong, E-mail: lh78553@126.com
- 基金信息：
  
  国家重点研发计划(2018YFC0116702);重庆市研究生教育教学改革研究项目(yjg202039);国家自然科学基金面上项目(82171265);“打仗型”医院军事医学科技创新专项(2023DZK77006)
- DOI：10.16016/j.2097-0927.202302091
  中图分类号： R614.2; R619+.9; TP183
- 收稿：2023-01-11，
  
  修回：2023-02-15，
  
  纸质出版：2023-04-30
- 稿件说明：
移动端阅览
易斌, 李洪. 围手术期大数据的应用与展望[J]. 陆军军医大学学报, 2023,45(8):725-731.

Bin YI, Hong LI. Application and prospect of big data during perioperative period[J]. Journal of Army Medical University, 2023, 45(8): 725-731.
易斌, 李洪. 围手术期大数据的应用与展望[J]. 陆军军医大学学报, 2023,45(8):725-731. DOI： 10.16016/j.2097-0927.202302091.

Bin YI, Hong LI. Application and prospect of big data during perioperative period[J]. Journal of Army Medical University, 2023, 45(8): 725-731. DOI： 10.16016/j.2097-0927.202302091.

摘要

目前医疗已经进入大数据时代，越来越多医疗大数据集被用于生成算法模型，以指导医疗护理的质量改进。我国每年实施数千万量级手术量，形成的围手术期大数据经过治理挖掘和算法应用后有望显著优化围手术期管理、减少并发症的发生。围手术期大数据是患者围手术期诊疗过程中产生的海量数据集合，结合大数据治理技术和机器学习算法，可应用于术前评估、不良事件预测、麻醉深度评估、自动给药系统、决策支持系统和超声图像处理等，从而显著提升手术患者围手术期安全，并减少医疗费用支出，由此提高人民群众的健康水平和减轻国家卫生经济负担。

Abstract

At present

medical treatment has entered an era of big data

and more and more medical big data sets have been used to generate algorithm models to guide the quality improvement of medical care. Tens of millions of operations are carried out in China every year

and the obtained perioperative big data are expected to significantly optimize perioperative management and reduce complications after governance mining and algorithm application. Perioperative big data are a massive data set generated during the perioperative diagnosis and treatment of patients. Combined with big data governance technology and machine learning algorithm

it can be applied to preoperative assessment

adverse event prediction

depth evaluation of anesthesia

automatic drug delivery system

decision support system and ultrasonic image processing

so as to significantly improve the perioperative safety of surgical patients and reduce medical expenses

and thus improve the health level of the people and reduce the burden of national health economy.

关键词

Keywords

references

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