Contextual Entity Ruler in Spark NLP refines entity recognition by applying context-aware rules to detected entities. It updates entities using customizable patterns, regex, and scope windows. It boosts accuracy by reducing false positives and adapting to niche contexts. Key features include token/character-based windows, prefix/suffix rules, and modes. Integrate it post-NER to enhance precision without retraining models.
Contextual Entity Ruler in Spark NLP refines entity recognition by applying context-aware rules to detected entities. It updates entities using customizable patterns, regex, and scope windows. It boosts accuracy by...
De-identification is the process of removing or masking sensitive, personally identifiable information from data, particularly in medical records. This is crucial to protect patient privacy and comply with regulations like...
John Snow Labs' Medical Language Models library is an excellent choice for leveraging the power of large language models (LLM) and natural language processing (NLP) in Azure Fabric due to...
Generative AI in healthcare is a transformative technology that utilizes advanced algorithms to synthesize and analyze medical data, facilitating personalized and efficient patient care. Generative AI has shown its effectiveness...
This post explores how John Snow Labs’ Healthcare NLP & LLM library can be used to extract genes and phenotypes from clinical text. By leveraging NLP techniques, we can transform...
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