可计算的临床证据综合:方法与进展

doi:10.3969/j.issn.1006-7795.2022.04.011

首都医科大学学报 ›› 2022, Vol. 43 ›› Issue (4): 576-583.doi: 10.3969/j.issn.1006-7795.2022.04.011

• 医学信息学:应用和发展 • 上一篇下一篇

可计算的临床证据综合:方法与进展

白永梅^1,2,3, 杜建^2*

1.北京大学医学部医学技术研究院,北京 100191;
2.北京大学健康医疗大数据国家研究院,北京 100191;
3.北京大学医学部公共卫生学院,北京 100191

收稿日期:2022-03-21 出版日期:2022-08-21 发布日期:2022-10-28
基金资助:
国家自然科学基金面上项目(72074006),中国科协青年人才托举工程项目(2017QNRC001),北京大学医学部人才启动经费项目(BMU2021YJ008)。

Computable clinical evidence synthesis:a literature review

Bai Yongmei ^1,2,3, Du Jian ^2*

1. Institute of Medical Technology, Peking University Health Science Center, Peking University, Beijing 100191, China;
2. National Institute of Health Data Science, Peking University, Beijing 100191, China;
3. School of Public Health, Peking University, Beijing 100191, China

Received:2022-03-21 Online:2022-08-21 Published:2022-10-28
Contact: *E-mail:dujian@bjmu.edu.cn
Supported by:
This study was supported by National Natural Science Foundation of China (72074006), Young Elite Scientists Sponsorship Program by China Association for Science and Technology (2017QNRC001), Peking University Health Science Center(BMU2021YJ008).

摘要/Abstract

摘要： 医学出版物的快速增长,医疗数据的不断增加,为进行快速医学证据综合带来了前所未有的挑战。近年来,如何跟上海量医学证据的发展,并将其转化为临床实践是一个迫切需要解决的问题。充分利用结构化医学数据库,直接利用结构化数据促进医学证据合成已成为临床证据综合的一大趋势,尤其是在新型冠状病毒肺炎(COVID-19,以下简称新冠肺炎)疫情严峻的形势下,通过将人读的以PDF和HTML为主要格式的医学证据转化为机器可读的格式,构建医学知识图谱,服务国内医生快速了解医学研究进展,对于开展快速证据综合,支持循证临床决策具有十分重要的意义。本文介绍了当前针对系统综述和临床试验开展可计算证据综合的研究现状,并通过案例分析来明确可计算证据综合的实现框架和未来发展方向。

关键词: 可计算, 证据综合, 随机对照试验, 系统综述

Abstract: The rapid growth of medical publications and the increasing medical data have brought unprecedented difficulties for rapid clinical evidence synthesis. In recent years, how to keep up with the development of massive medical evidence and convert it into clinical practice has become an urgent problem to be solved. At present, the method of making full use of structured medical database and directly using structured data to promote the synthesis of medical evidence has become a major trend in clinical evidence synthesis. By converting human-readable medical evidence in PDF and HTML formats into machine-readable format, we can construct a graph of medical knowledge to help domestic doctors to quickly understand the medical research progress and carry out evidence synthesis to support evidence-based clinical decision-making. We introduced the current research status of computable evidence synthesis for system review and clinical trials and clarified the implementation framework and future development direction of computable evidence synthesis through case analysis.

Key words: computable, evidence synthesis, randomized controlled trial(RCT), systematic review

中图分类号:

G201

白永梅, 杜建. 可计算的临床证据综合:方法与进展[J]. 首都医科大学学报, 2022, 43(4): 576-583.

Bai Yongmei, Du Jian. Computable clinical evidence synthesis:a literature review[J]. Journal of Capital Medical University, 2022, 43(4): 576-583.

参考文献

[1] Donnelly C A, Boyd I, Campbell P, et al. Four principles to make evidence synthesis more useful for policy[J]. Nature, 2018, 558(7710):361－364.
[2] Elliott J, Lawrence R, Minx J C, et al. Decision makers need constantly updated evidence synthesis[J]. Nature, 2021, 600(7889):383-385.
[3] Bastian H, Glasziou P, Chalmers I. Seventy-five trials and eleven systematic reviews a day: how will we ever keep up?[J]. PLoS Med, 2010, 7(9):e1000326.
[4] Borah R, Brown A W, Capers P L, et al. Analysis of the time and workers needed to conduct systematic reviews of medical interventions using data from the PROSPERO registry[J]. BMJ Open, 2017, 7(2):e012545.
[5] Vardakas K Z, Tsopanakisc G, Poulopoulouc A, et al. An analysis of factors contributing to PubMed's growth[J]. J Informetr, 2015, 9(3):592－617.
[6] Dunn A G, Bourgeois F T. Is it time for computable evidence synthesis?[J]. J Am Med Inform Assoc, 2020, 27(6):972－975.
[7] Sebire N J, Cake C, Morris A D. HDR UK supporting mobilising computable biomedical knowledge in the UK[J]. BMJ Health Care Inform, 2020, 27(2):e100122.
[8] 杜建, 孔桂兰, 李鹏飞, 等. 可计算医学知识的基本概念与实现路径[J]. 情报学报, 2021, 40(11):1221－1233.
[9] Flynn A J, Friedman C P, Boisvert P, et al. The Knowledge Object Reference Ontology (KORO):a formalism to support management and sharing of computable biomedical knowledge for learning health systems[J]. Learn Health Syst, 2018, 2(2):e10054.
[10] Bodenreider O. Biomedical ontologies in action: role in knowledge management, data integration and decision support[J]. Yearb Med Inform, 2008, 47(Suppl 1):S67－S79.
[11] Kang B, Yoon J, Kim H Y, et al. Deep-learning-based automated terminology mapping in OMOP-CDM[J]. J Am Med Inform Assoc, 2021, 28(7):1489－1496.
[12] Wang L, Xie H M, Han W T, et al. Construction of a knowledge graph for diabetes complications from expert-reviewed clinical evidences[J]. Comput Assist Surg, 2020, 25(1):29－35.
[13] Nye B, Jessy Li J, Patel R, et al. A corpus with multi-level annotations of patients, interventions and outcomes to support language processing for medical literature[J]. Proc Conf Assoc Comput Linguist Meet, 2018,2018:197－207.
[14] Marshall I J, Nye B, Kuiper J, et al. Trialstreamer: a living, automatically updated database of clinical trial reports[J]. J Am Med Inform Assoc, 2020, 27(12):1903－1912.
[15] Tasneem A, Aberle L, Ananth H, et al. The database for aggregate analysis of ClinicalTrials.gov (AACT) and subsequent regrouping by clinical specialty[J]. PLoS One, 2012, 7(3):e33677.
[16] Du J C, Wang Q, Wang J Q, et al. COVID-19 trial graph: a linked graph for COVID－19 clinical trials[J]. J Am Med Inform Assoc, 2021, 28(9):1964－1969.
[17] Chen Z Q, Peng B, Ioannidis V N, et al. A knowledge graph of clinical trials ([Formula: see text])[J]. Sci Rep, 2022, 12(1):4724.
[18] Nye B E, Nenkova A, Marshall I J, et al. Trialstreamer: mapping and browsing medical evidence in real-time[J]. Proc Conf, 2020, 2020: 63－69.
[19] 王杨, 郎欣月, 朱熠冰, 等. 临床意义与统计学意义结合的临床试验结果分类方法及评价研究[J]. 中华流行病学杂志, 2021, 42(7):1280－1285.
[20] Mayer T, Marro S, Cabrio E, et al. Enhancing evidence-based medicine with natural language argumentative analysis of clinical trials[J]. Artif Intell Med, 2021, 118: 102098.
[21] Miron L, Gonçalves R S, Musen M A. Obstacles to the reuse of study metadata in ClinicalTrials.gov[J]. Sci Data, 2020, 7(1):443.
[22] Liu Y, Elsworth B, Erola P, et al. EpiGraphDB: a database and data mining platform for health data science[J]. Bioinformatics, 2021, 37(9):1304－1311.
[23] Huang H C, Dong Z Y. Research on architecture and query performance based on distributed graph database Neo4j[C]//2013 3rd International Conference on Consumer Electronics, Communications and Networks. New York, USA: IEEE, 2013: 533－536.
[24] Francis N, Green A, Guagliardo P, et al. Cypher: an evolving query language for property graphs[C]//Proceedings of the 2018 International Conference on Management of Data. Houston,TX:ACM,2018:1433-1445.
[25] Hripcsak G, Duke J D, Shah N H, et al. Observational Health Data Sciences and Informatics (OHDSI):opportunities for observational researchers[C]//15th World Congress on Health and Biomedical Informatics, MEDINFO 2015. Sao Paulo: IOS Press, 2015: 574－578.
[26] Alper B S, Dehnbostel J, Afzal M, et al. Making science computable: developing code systems for statistics, study design, and risk of bias[J]. J Biomed Inform, 2021, 115: 103685.
[27] Williams M, Richesson R L, Bray B E, et al. Summary of third annual MCBK public meeting: mobilizing computable biomedical knowledge-accelerating the second knowledge revolution[J]. Learn Health Syst, 2020, 5(1):e10255.
[28] Morales D R, Conover M M, You S C, et al. Renin-angiotensin system blockers and susceptibility to COVID－19: an international, open science, cohort analysis[J]. Lancet Digit Health, 2021, 3(2):e98－e114.

可计算的临床证据综合:方法与进展

Computable clinical evidence synthesis:a literature review

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