How John Snow Labs' EntityRulerInternal extracts medical named entities with precision. The advanced features of Healthcare NLP for medical data processing
EntityRulerInternal in Spark NLP extracts medical entities from text using regex patterns or exact matches defined in JSON or CSV files. With practical examples, this post explains how to set...
The RegexMatcherInternal class leverages the power of regular expressions to identify and associate specific patterns within text data with predefined entities, such as dates, SSNs, and email addresses. This method...
This talk presents new levels of accuracy that have very recently been achieved, on public and independently reproducible benchmarks, on the three most common use cases for language models in...
Accurate Extraction of Response to Treatment Indications in Oncology Accurate assessment of patient response to cancer treatment is paramount in guiding clinical decision-making and optimizing therapeutic outcomes. While large language...
In this post, we explore the utilization of pre-trained models within the Healthcare NLP library by John Snow Labs to map medical terminology to the MedDRA ontology. Specifically, our aim...
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