scientific-clinical-nlp

To extract clinical entities and detect negation from electronic health records, you can use a pipeline built with MedSpaCy and scispaCy. It identifies medical entities, applies NegEx or ConText for negation detection, and segments clinical note sections automatically.

Yes, you can de-identify Protected Health Information (PHI) in clinical notes for data privacy. The pipeline includes PHI redaction capabilities to anonymize patient data before or during the clinical entity extraction and terminology linking process.

To map extracted clinical concepts to standardized vocabularies, the pipeline uses a medical terminology linker. It bridges recognized entities to UMLS, SNOMED-CT, and ICD-10 codes, enabling accurate terminology matching for downstream biomedical analysis.

Yes, the clinical NLP pipeline supports automatic section classification for medical notes. It segments unstructured clinical text into relevant sections, improving the context accuracy of entity recognition and negation detection.

The best way to process biomedical literature for medical entity recognition is using scispaCy and MedSpaCy models. They provide specialized NLP pipelines to accurately identify clinical entities, handle negation, and link biomedical terms to standardized vocabularies.