The data (demographics, emergency department letters, discharge summaries, clinical notes, lab results, vital signs) were retrieved and analyzed in near real-time from the structured and unstructured components of the electronic health record (EHR) using a variety of natural language processing (NLP) informatics tools belonging to the CogStack ecosystem, namely MedCAT and MedCATTrainer. The CogStack NLP pipeline captures negation, synonyms, and acronyms for medical SNOMED-CT concepts as well as surrounding linguistic context using deep learning and long short-term memory networks. MedCAT produces unsupervised annotations for all SNOMED-CT concepts under parent terms Clinical Finding, Disorder, Organism, and Event with disambiguation, pre-trained on MIMIC-III. The annotated SNOMED-CT terms are summarised below
Carr E, Bendayan R, Bean D, OGallagher K, Pickles A, Stahl D, Zakeri R, Searle T, Shek A, Kraljevic Z, Teo J, Shah A, Dobson R. (2020) Supplementing the National Early Warning Score (NEWS2) for anticipating early deterioration among patients with COVID-19 infection. medRxiv DOI: 10.1101/2020.04.24.20078006
|230572002||Neuropathy co-occurrent and due to diabetes mellitus (disorder)|
|237599002||Insulin treated type 2 diabetes mellitus (disorder)|
|44054006||Diabetes mellitus type 2 (disorder)|
|49455004||Polyneuropathy co-occurrent and due to diabetes mellitus (disorder)|
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