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_heartfailure_eJywGK6XvyoSe4SBhSexb3_SNOMEDCT||-||Expression Select||Add codes||
|10633002||Acute congestive heart failure (disorder)|
|418304008||Diastolic heart failure (disorder)|
|42343007||Congestive heart failure (disorder)|
|426263006||Congestive heart failure due to left ventricular systolic dysfunction (disorder)|
|48447003||Chronic heart failure (disorder)|
To Export Concept Details:
To Export Concept Code List:
|2677||Heart failure - Secondary care||
The data (demographics, emergency department lett…
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Components are individual rules matching a set of codes. One or more components are used to define the codes contained within a concept.