Introducing Information Extraction to Radiology Information Systems to Improve the Efficiency on Reading Reports.
Background: Radiology reports are a permanent record of patient's health information often used in clinical practice and research. Reading radiology reports is common for clinicians and radiologists. However, it is laborious and time-consuming when the amount of reports to be read is large. Assistin...
| Published in: | Methods of Information in Medicine Vol. 58; no. 2/3; pp. 94 - 107 |
|---|---|
| Main Authors: | , , , , , , , |
| Format: | research Journal Article |
| Published: |
Thieme Medical Publishing Inc.
2019
|
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=138593736&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 138593736 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00261270 W7M jtl: Methods of Information in Medicine issn: 00261270 maglogo: N pubinfo: dt: 2019 vid: 58 iid: 2/3 pid: 2811 pub: Thieme Medical Publishing Inc. place: New York, New York artinfo: ui: 138593736 138593736 NLM31514210 138593736 10.1055/s-0039-1694992 NLM31514210 138593736 ppf: 94 ppct: 13 formats: tig: atl: Introducing Information Extraction to Radiology Information Systems to Improve the Efficiency on Reading Reports. aug: au: Xie, Zhe Yang, Yuanyuan Wang, Mingqing Li, Ming Huang, Haozhe Zheng, Dezhong Shu, Rong Ling, Tonghui affil: Shanghai Institute of Technical Physics, University of Chinese Academy of Sciences, Beijing, People's Republic of China sug: subj: Reading Reports Radiology Information Systems Human Data Mining Models, Theoretical Hospitals Validation Studies Comparative Studies Evaluation Research Multicenter Studies Scales ab: Background: Radiology reports are a permanent record of patient's health information often used in clinical practice and research. Reading radiology reports is common for clinicians and radiologists. However, it is laborious and time-consuming when the amount of reports to be read is large. Assisting clinicians to locate and assimilate the key information of reports is of great significance for improving the efficiency of reading reports. There are few studies on information extraction from Chinese medical texts and its application in radiology information systems (RIS) for efficiency improvement.Objectives: The purpose of this study was to explore methods for extracting, grouping, ranking, delivering, and displaying medical-named entities in radiology reports which can yield efficiency improvement in RISs.Methods: A total of 5,000 reports were obtained from two medical institutions for this study. We proposed a neural network model called Multi-Embedding-BGRU-CRF (bidirectional gated recurrent unit-conditional random field) for medical-named entity recognition and rule-based methods for entity grouping and ranking. Furthermore, a methodology for delivering and displaying entities in RISs was presented.Results: The proposed neural named entity recognition model has achieved a good F1 score of 95.88%. Entity ranking achieved a very high accuracy of 99.23%. The weakness of the system is the entity grouping approach which yield accuracy of 91.03%. The effectiveness of the overall solution was proved by an evaluation task performed by two clinicians based on the setup of actual clinical practice.Conclusions: The neural model shows great potential in extracting medical-named entities from radiology reports, especially for languages, that lack lexicons and natural language processing tools. The pipeline of extracting, grouping, ranking, delivering, and displaying medical-named entities could be a feasible solution to enhance RIS functionality by information extraction. The integration of information extraction and RIS has been demonstrated to be effective in improving the efficiency of reading radiology reports. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|