Construction of multi-parameter emergency database and preliminary application research_Journal of Biomedical Engineering

Authors：

WANG Junmei ¹ , LIU Tongbo ² , SUN Yuyao ³ , LI Peiyao ⁴ , ZHAO Yuzhuo ⁵ ,  ZHANG Zhengbo ^4,6,7 , XUE Wanguo ⁶ , LI Tanshi ⁵ , CAO Desen ^4,7

1. School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, P.R.China;
2. Computer Department, Chinese PLA General Hospital, Beijing 100853, P.R.China;
3. School of Software, Southeast University, Suzhou, Jiangsu 215123, P.R.China;
4. Medical Engineering Support Center, Chinese PLA General Hospital, Beijing 100853, P.R.China;
5. Emergency Department, Chinese PLA General Hospital, Beijing 100853, P.R.China;
6. Medical Big Data Center, Chinese PLA General Hospital, Beijing 100853, P.R.China;
7. Medical Device Research and Development and Evaluation Center, Chinese PLA General Hospital, Beijing 100853, P.R.China;

Corresponding author：

ZHANG Zhengbo, Email: zhengbozhang@126.com

Keywords：

emergency database; acute and critical illness; medical database; clinical decision making

DOI：

10.7507/1001-5515.201809032

Video：

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The analysis of big data in medical field cannot be isolated from the high quality clinical database, and the construction of first aid database in our country is still in the early stage of exploration. This paper introduces the idea and key technology of the construction of multi-parameter first aid database. By combining emergency business flow with information flow, an emergency data integration model was designed with reference to the architecture of the Medical Information Mart for Intensive Care III (MIMIC-III), created by Computational Physiology Laboratory of Massachusetts Institute of Technology (MIT), and a high-quality first-aid database was built. The database currently covers 22 941 medical records for 19 814 different patients from May 2015 to October 2017, including relatively complete information on physiology, biochemistry, treatment, examination, nursing, etc. And based on the database, the first First-Aid Big Data Datathon event, which 13 teams from all over the country participated in, was launched. The First-Aid database provides a reference for the construction and application of clinical database in China. And it could provide powerful data support for scientific research, clinical decision making and the improvement of medical quality, which will further promote secondary analysis of clinical data in our country.

Citation： WANG Junmei, LIU Tongbo, SUN Yuyao, LI Peiyao, ZHAO Yuzhuo, ZHANG Zhengbo, XUE Wanguo, LI Tanshi, CAO Desen. Construction of multi-parameter emergency database and preliminary application research. Journal of Biomedical Engineering, 2019, 36(5): 818-826, 833. doi: 10.7507/1001-5515.201809032 Copy

1.	Zhang Yue, Guo Shuli, Han Lina, et al. Application and exploration of big data mining in clinical medicine. Chin Med J, 2016, 129(6): 731-738.
2.	Saeed M, Villarroel M, Reisner A T, et al. Multiparameter intelligent monitoring in intensive care II (MIMIC-II): A public-access intensive care unit database. Crit Care Med, 2011, 39(5): 952-960.
3.	Ghassemi M, Celi L A, Stone D J. State of the art review: the data revolution in critical care. Crit Care, 2015, 19(1): 118.
4.	Newgard C D, Zive D, Malveau S, et al. Developing a statewide emergency medical services database linked to hospital outcomes: a feasibility study. Prehospital Emergency Care, 2011, 15(3): 303-319.
5.	Heldeweg M L A, Liu N, Koh Z X, et al. A novel cardiovascular risk stratification model incorporating ECG and heart rate variability for patients presenting to the emergency department with chest pain. Critical Care, 2016, 20(1): 179.
6.	Horng S, Sontag D A, Halpern Y, et al. Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning. PLoS One, 2017, 12(4): e0174708.
7.	周永, 王伊龙, 吴敌, 等. 卒中患者院前转运与延误现况调查——一项来自中国七城市卒中事件急诊登记数据库研究 (CARDs). 中国全科医学, 2009, 12(2): 96-99.
8.	宁晔, 蒋崇慧, 尹刚, 等. 建立中山市创伤数据库的探索性研究. 岭南急诊医学杂志, 2014, 19(4): 318-319.
9.	邱俊, 袁丹凤, 姚远, 等. 中国创伤数据库建设历史与现状. 创伤与急危重病医学, 2014, 2(1): 10-13, 29.
10.	成亮, 朱勇, 龙海涛, 等. 湖南省区域性骨盆创伤数据库的研制. 中南大学学报: 医学版, 2017, 42(4): 445-450.
11.	杜欣为, 陆兆辉. 儿童心胸外专科数据库建设的单中心经验与展望. 智慧健康, 2016, 2(11): 26-31.
12.	顾颖. 专科病例数据库建设现状与对策. 中华医学图书情报杂志, 2011, 20(11): 20-22.
13.	Johnson A E W, Pollard T J, Shen L, et al. MIMIC-III, a freely accessible critical care database. Sci Data, 2016, 3: 160035.
14.	Tang Qin, Zhang Qiong, Lv Yao, et al. SEGreg: a database for human specifically expressed genes and their regulations in cancer and normal tissue. Brief Bioinform, 2018: 1-7.
15.	成亮, 朱勇, 龙海涛, 等. 湖南省区域性骨盆创伤数据库的研制. 中南大学学报(医学版), 2017, 42(4): 445-450.
16.	顾大川, 郑哲, 赵韡, 等. 中国成人心血管外科注册登记数据库的构建. 中国循环杂志, 2017, 32(10): 1010-1014.
17.	Silber J H, Rosenbaum P R, Schwartz J S, et al. Evaluation of the complication rate as a measure of quality of care in coronary artery bypass graft surgery. JAMA, 1995, 4(6): 317.
18.	Khan N A, Quan H, Bugar J M, et al. Association of postoperative complications with hospital costs and length of stay in a tertiary care center. J Gen Intern Med, 2006, 21(2): 177–180.
19.	Lagoe R J, Johnson P E, Murphy M P. Inpatient hospital complications and lengths of stay: a short report. BMC Res Notes, 2011, 4(1): 1-5.

1. Zhang Yue, Guo Shuli, Han Lina, et al. Application and exploration of big data mining in clinical medicine. Chin Med J, 2016, 129(6): 731-738.
2. Saeed M, Villarroel M, Reisner A T, et al. Multiparameter intelligent monitoring in intensive care II (MIMIC-II): A public-access intensive care unit database. Crit Care Med, 2011, 39(5): 952-960.
3. Ghassemi M, Celi L A, Stone D J. State of the art review: the data revolution in critical care. Crit Care, 2015, 19(1): 118.
4. Newgard C D, Zive D, Malveau S, et al. Developing a statewide emergency medical services database linked to hospital outcomes: a feasibility study. Prehospital Emergency Care, 2011, 15(3): 303-319.
5. Heldeweg M L A, Liu N, Koh Z X, et al. A novel cardiovascular risk stratification model incorporating ECG and heart rate variability for patients presenting to the emergency department with chest pain. Critical Care, 2016, 20(1): 179.
6. Horng S, Sontag D A, Halpern Y, et al. Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning. PLoS One, 2017, 12(4): e0174708.
7. 周永, 王伊龙, 吴敌, 等. 卒中患者院前转运与延误现况调查——一项来自中国七城市卒中事件急诊登记数据库研究 (CARDs). 中国全科医学, 2009, 12(2): 96-99.
8. 宁晔, 蒋崇慧, 尹刚, 等. 建立中山市创伤数据库的探索性研究. 岭南急诊医学杂志, 2014, 19(4): 318-319.
9. 邱俊, 袁丹凤, 姚远, 等. 中国创伤数据库建设历史与现状. 创伤与急危重病医学, 2014, 2(1): 10-13, 29.
10. 成亮, 朱勇, 龙海涛, 等. 湖南省区域性骨盆创伤数据库的研制. 中南大学学报: 医学版, 2017, 42(4): 445-450.
11. 杜欣为, 陆兆辉. 儿童心胸外专科数据库建设的单中心经验与展望. 智慧健康, 2016, 2(11): 26-31.
12. 顾颖. 专科病例数据库建设现状与对策. 中华医学图书情报杂志, 2011, 20(11): 20-22.
13. Johnson A E W, Pollard T J, Shen L, et al. MIMIC-III, a freely accessible critical care database. Sci Data, 2016, 3: 160035.
14. Tang Qin, Zhang Qiong, Lv Yao, et al. SEGreg: a database for human specifically expressed genes and their regulations in cancer and normal tissue. Brief Bioinform, 2018: 1-7.
15. 成亮, 朱勇, 龙海涛, 等. 湖南省区域性骨盆创伤数据库的研制. 中南大学学报(医学版), 2017, 42(4): 445-450.
16. 顾大川, 郑哲, 赵韡, 等. 中国成人心血管外科注册登记数据库的构建. 中国循环杂志, 2017, 32(10): 1010-1014.
17. Silber J H, Rosenbaum P R, Schwartz J S, et al. Evaluation of the complication rate as a measure of quality of care in coronary artery bypass graft surgery. JAMA, 1995, 4(6): 317.
18. Khan N A, Quan H, Bugar J M, et al. Association of postoperative complications with hospital costs and length of stay in a tertiary care center. J Gen Intern Med, 2006, 21(2): 177–180.
19. Lagoe R J, Johnson P E, Murphy M P. Inpatient hospital complications and lengths of stay: a short report. BMC Res Notes, 2011, 4(1): 1-5.

Journal of Biomedical Engineering

Construction of multi-parameter emergency database and preliminary application research

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