Introduction If you were a doctor consulting with another doctor, you might naturally say, “I had a case like that three years ago, and here’s what I did.” You wouldn’t...
PHI De-Identification with State-of-the-Art NLP De-identification for natural language processing in healthcare is a critical procedure for safeguarding Protected Health Information (PHI) within clinical notes, wherein the data is anonymized...
Spark NLP for Healthcare NER models outperform ChatGPT by 10–45% on key medical concepts, resulting in half the errors compared to ChatGPT. Introduction In the last few months, large language...
In assigning ICD10-CM codes, Spark NLP for Healthcare achieved a 76% success rate, while GPT-3.5 and GPT-4 had overall accuracies of 26% and 36% respectively. Introduction In the healthcare industry,...
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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