FRINK Linked Data Fragments server

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• W3016865395 abstract "Structured reporting is a favorable and sustainable form of reporting in radiology. Among its advantages are better presentation, clearer nomenclature, and higher quality. By using MRRT-compliant templates, the content of the categorized items (e.g., select fields) can be automatically stored in a database, which allows further research and quality analytics based on established ontologies like RadLex® linked to the items. Additionally, it is relevant to provide free-text input for descriptions of findings and impressions in complex imaging studies or for the information included with the clinical referral. So far, however, this unstructured content cannot be categorized. We developed a solution to analyze and code these free-text parts of the templates in our MRRT-compliant reporting platform, using natural language processing (NLP) with RadLex® terms in addition to the already categorized items. The established hybrid reporting concept is working successfully. The NLP tool provides RadLex® codes with modifiers (affirmed, speculated, negated). Radiologists can confirm or reject codes provided by NLP before finalizing the structured report. Furthermore, users can suggest RadLex® codes from free text that is not correctly coded with NLP or can suggest to change the modifier. Analyzing free-text fields took 1.23 s on average. Hybrid reporting enables coding of free-text information in our MRRT-compliant templates and thus increases the amount of categorized data that can be stored in the database. This enhances the possibilities for further analyses, such as correlating clinical information with radiological findings or storing high-quality structured information for machine-learning approaches." @default.
• W3016865395 created "2020-04-24" @default.
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• W3016865395 date "2020-04-21" @default.
• W3016865395 modified "2023-09-29" @default.
• W3016865395 title "A Hybrid Reporting Platform for Extended RadLex Coding Combining Structured Reporting Templates and Natural Language Processing" @default.
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• W3016865395 doi "https://doi.org/10.1007/s10278-020-00342-0" @default.
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