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本文引用的文献

1
Detecting possible vaccination reactions in clinical notes.在临床记录中检测可能的疫苗接种反应。
AMIA Annu Symp Proc. 2005;2005:306-10.
2
Natural language processing in the electronic medical record: assessing clinician adherence to tobacco treatment guidelines.电子病历中的自然语言处理：评估临床医生对烟草治疗指南的依从性。
Am J Prev Med. 2005 Dec;29(5):434-9. doi: 10.1016/j.amepre.2005.08.007.
3
Prospective recruitment of patients with congestive heart failure using an ad-hoc binary classifier.使用特设二元分类器对充血性心力衰竭患者进行前瞻性招募。
J Biomed Inform. 2005 Apr;38(2):145-53. doi: 10.1016/j.jbi.2004.11.016.
4
Multidimensional text classification for drug information.药物信息的多维度文本分类
IEEE Trans Inf Technol Biomed. 2004 Sep;8(3):306-12. doi: 10.1109/titb.2004.832542.
5
Automated encoding of clinical documents based on natural language processing.基于自然语言处理的临床文档自动编码
J Am Med Inform Assoc. 2004 Sep-Oct;11(5):392-402. doi: 10.1197/jamia.M1552. Epub 2004 Jun 7.
6
The clinician's perspective on electronic health records and how they can affect patient care.临床医生对电子健康记录及其如何影响患者护理的看法。
BMJ. 2004 May 15;328(7449):1184-7. doi: 10.1136/bmj.328.7449.1184.
7
IndexFinder: a method of extracting key concepts from clinical texts for indexing.索引查找器：一种从临床文本中提取关键概念以进行索引的方法。
AMIA Annu Symp Proc. 2003;2003:763-7.
8
Are electronic medical records trustworthy? Observations on copying, pasting and duplication.电子病历是否可信？关于复制、粘贴和重复使用的观察
AMIA Annu Symp Proc. 2003;2003:269-73.
9
A pilot study of contextual UMLS indexing to improve the precision of concept-based representation in XML-structured clinical radiology reports.一项关于上下文统一医学语言系统（UMLS）索引编制的初步研究，以提高基于概念的XML结构化临床放射学报告表示的精确性。
J Am Med Inform Assoc. 2003 Nov-Dec;10(6):580-7. doi: 10.1197/jamia.M1369. Epub 2003 Aug 4.
10
The role of domain knowledge in automating medical text report classification.领域知识在医学文本报告分类自动化中的作用。
J Am Med Inform Assoc. 2003 Jul-Aug;10(4):330-8. doi: 10.1197/jamia.M1157. Epub 2003 Mar 28.

MediClass：一种用于在任何电子病历中检测和分类基于诊疗过程的临床事件的系统。

MediClass: A system for detecting and classifying encounter-based clinical events in any electronic medical record.

作者信息

Hazlehurst Brian, Frost H Robert, Sittig Dean F, Stevens Victor J

机构信息

Center for Health Research, 3800 N. Interstate Ave., Portland, OR 97227, USA.

出版信息

J Am Med Inform Assoc. 2005 Sep-Oct;12(5):517-29. doi: 10.1197/jamia.M1771. Epub 2005 May 19.

DOI:10.1197/jamia.M1771
PMID:15905485
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC1205600/
Abstract

MediClass is a knowledge-based system that processes both free-text and coded data to automatically detect clinical events in electronic medical records (EMRs). This technology aims to optimize both clinical practice and process control by automatically coding EMR contents regardless of data input method (e.g., dictation, structured templates, typed narrative). We report on the design goals, implemented functionality, generalizability, and current status of the system. MediClass could aid both clinical operations and health services research through enhancing care quality assessment, disease surveillance, and adverse event detection.

摘要

MediClass是一个基于知识的系统，它处理自由文本和编码数据，以自动检测电子病历（EMR）中的临床事件。该技术旨在通过自动对EMR内容进行编码，优化临床实践和过程控制，而不管数据输入方法如何（例如听写、结构化模板、打字叙述）。我们报告了该系统的设计目标、实现的功能、通用性和当前状态。MediClass可以通过加强护理质量评估、疾病监测和不良事件检测，辅助临床操作和卫生服务研究。