Meta-generalis: A novel method for structuring information from radiology reports

Barbosa, Flávio de Aguiar; Traina, Agma Jucci; Muglia, Valdair Francisco

doi:10.4338/aci-2016-03-ra-0037

Cited by 5 publications

(1 citation statement)

References 19 publications

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“…5 Metageneralis introduce a lexicon-based method to extract and normalize lexicon units from magnetic resonance imaging (MRI) reports. 6 Lakhani et al uses a rule-based method to detect critical results from radiology reports. 7 Over the past decade, researchers have paid more attention to machine-learning technique.…”

Section: Introductionmentioning

confidence: 99%

Introducing Information Extraction to Radiology Information Systems to Improve the Efficiency on Reading Reports

et al. 2019

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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.

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Section: Introductionmentioning

confidence: 99%