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Article Abstract
Building Clinical Decision Support Systems, whether from regression models or machine learning requires clinical data either in standard terminology or as text for Natural Language Processing (NLP). Unfortunately, many clinical notes are written quickly during the consultation and contain many abbreviations, typographical errors, and a lack of grammar and punctuation Processing these highly unstructured clinical notes is an open challenge for NLP that we address in this paper. We present RECAP-KG - a knowledge graph construction frame workfrom primary care clinical notes. Our framework extracts structured knowledge graphs from the clinical record by utilising the SNOMED-CT ontology both the entire finding hierarchy and a COVID-relevant curated subset. We apply our framework to consultation notes in the UK COVID-19 Clinical Assessment Service (CCAS) dataset and provide a quantitative evaluation of our framework demonstrating that our approach has better accuracy than traditional NLP methods when answering questions about patients.
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