The development of high-quality Deep Learning NLP models usually requires significant amounts of training data. The models must be taught to correctly differentiate specific entities and make accurate predictions. This is usually done via examples (training data) provided by human users, with good expertise in the target domain. The best and easiest way to put together the example data is via (manual) annotation.
Use pretrained models, segment texts into words, and train custom word segmenter models with Python TL; DR: Some Asian languages don’t separate words by white space like English, and NLP...
Voice of Patients (VoP) NER, a brand-new Named Entity Recognition (NER) model released by John Snow Labs, can extract clinical entities from patient forums much better than any other clinical...
De-Identification is a process that needs to be applied to de-identify (anonymize) or obfuscate (replace with fake entities) PHI (protected health information) data from clinical notes. Obfuscation of PHI entities ...
RxNorm provides normalized names for clinical drugs and links its names to many of the drug vocabularies commonly used in pharmacy management and drug interaction software. Entity Resolver pipeline in Healthcare...
Introduction According to Forbes, unstructured data is growing at 55-65% each year and almost 90% of it has been generated in the recent two years. More data demands the...
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