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临床概念提取：方法学综述。

Clinical concept extraction: A methodology review.

作者信息

Fu Sunyang, Chen David, He Huan, Liu Sijia, Moon Sungrim, Peterson Kevin J, Shen Feichen, Wang Liwei, Wang Yanshan, Wen Andrew, Zhao Yiqing, Sohn Sunghwan, Liu Hongfang

机构信息

Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, United States; University of Minnesota - Twin Cities, Minneapolis, MN 55455, United States.

Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, United States.

出版信息

J Biomed Inform. 2020 Sep;109:103526. doi: 10.1016/j.jbi.2020.103526. Epub 2020 Aug 6.

DOI:10.1016/j.jbi.2020.103526
PMID:32768446
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7746475/
Abstract

BACKGROUND

Concept extraction, a subdomain of natural language processing (NLP) with a focus on extracting concepts of interest, has been adopted to computationally extract clinical information from text for a wide range of applications ranging from clinical decision support to care quality improvement.

OBJECTIVES

In this literature review, we provide a methodology review of clinical concept extraction, aiming to catalog development processes, available methods and tools, and specific considerations when developing clinical concept extraction applications.

METHODS

Based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a literature search was conducted for retrieving EHR-based information extraction articles written in English and published from January 2009 through June 2019 from Ovid MEDLINE In-Process & Other Non-Indexed Citations, Ovid MEDLINE, Ovid EMBASE, Scopus, Web of Science, and the ACM Digital Library.

RESULTS

A total of 6,686 publications were retrieved. After title and abstract screening, 228 publications were selected. The methods used for developing clinical concept extraction applications were discussed in this review.

摘要

背景

概念提取是自然语言处理（NLP）的一个子领域，专注于提取感兴趣的概念，已被用于从文本中计算提取临床信息，以用于从临床决策支持到护理质量改善等广泛应用。

目的

在本综述中，我们对临床概念提取进行方法学综述，旨在梳理开发流程、可用方法和工具，以及开发临床概念提取应用时的具体注意事项。

方法

基于系统评价和Meta分析的首选报告项目（PRISMA）指南，进行文献检索，以检索2009年1月至2019年6月期间发表的、用英文撰写的基于电子健康记录（EHR）的信息提取文章，检索数据库包括Ovid MEDLINE在研及其他未索引引文、Ovid MEDLINE、Ovid EMBASE、Scopus、科学引文索引（Web of Science）和美国计算机协会数字图书馆（ACM Digital Library）。

结果

共检索到6686篇出版物。经标题和摘要筛选后，选定228篇出版物。本综述讨论了用于开发临床概念提取应用的方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c71e/7746475/e1cfc19efb7f/nihms-1622924-f0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c71e/7746475/29231b678dcf/nihms-1622924-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c71e/7746475/c21d149ae3f0/nihms-1622924-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c71e/7746475/9a3790f14056/nihms-1622924-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c71e/7746475/6d36c9027c5a/nihms-1622924-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c71e/7746475/1b976036e03d/nihms-1622924-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c71e/7746475/e1cfc19efb7f/nihms-1622924-f0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c71e/7746475/29231b678dcf/nihms-1622924-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c71e/7746475/c21d149ae3f0/nihms-1622924-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c71e/7746475/9a3790f14056/nihms-1622924-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c71e/7746475/6d36c9027c5a/nihms-1622924-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c71e/7746475/1b976036e03d/nihms-1622924-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c71e/7746475/e1cfc19efb7f/nihms-1622924-f0006.jpg

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