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Are Semantic Annotators Able to Extract Relevant Complexity-Related Concepts from Clinical Notes?

Abstract : Clinical decision support systems (CDSSs) implementing cancer clinical practice guidelines (CPGs) have the potential to improve the compliance of decisions made by multidisciplinary tumor boards (MTB) with CPGs. However, guideline-based CDSSs do not cover complex cases and need time for discussion. We propose to learn how to predict complex cancer cases prior to MTBs from breast cancer patient summaries (BCPSs) resuming clinical notes. BCPSs being unstructured natural language textual documents, we implemented four semantic annotators (ECMT, SIFR, cTAKES, and MetaMap) to assess whether complexity-related concepts could be extracted from clinical notes. On a sample of 24 BCPSs covering 35 complexity reasons, ECMT and MetaMap were the most efficient systems with a performance rate of 60% (21/35) and 49% (17/35), respectively. When using the four annotators in sequence, 69% of complexity reasons were extracted (24/35 reasons).
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https://hal.archives-ouvertes.fr/hal-03560230
Contributor : Akram REDJDAL Connect in order to contact the contributor
Submitted on : Monday, February 7, 2022 - 2:28:48 PM
Last modification on : Wednesday, February 9, 2022 - 3:46:14 AM

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Akram Redjdal, Jacques Bouaud, Joseph Gligorov, Brigitte Séroussi. Are Semantic Annotators Able to Extract Relevant Complexity-Related Concepts from Clinical Notes?. Applying the FAIR Principles to Accelerate Health Research in Europe in the Post COVID-19 Era, IOS Press, 2021, Studies in Health Technology and Informatics, ⟨10.3233/SHTI210836⟩. ⟨hal-03560230⟩

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