Spark NLP in Action

Run 300+ live demos and notebooks
Medical Large Language Models
Explore the use of Medical Large Language Models for tasks like Text Summarization, Generation, and Question Answering. (...)
Detect Entities in Clinical Text
Identify 77 entity types including Symptom, Treatments, Test, Oncological, Procedure, Diabetes, Drug, Dosage, Date, Imaging Finding, and more. (...)
Information Extraction in Oncology
Detect clinical entities and relationships related to cancer staging, grading, histology, tumor characteristics, biomarkers, treatments, and outcome measures. (...)
De-identify Clinical Notes in Different Languages
De-identify and obfuscate protected health information (PHI) in English, Spanish, French, Italian, Portuguese, Romanian, and German texts. (...)
Adverse Drug Event Detection
Detect adverse reactions from drugs described in the clinical text, online reviews, and social media posts. (...)
Voice of the Patients
Extract and classify healthcare-related terms from documents written by patient such as questions, reviews, messages, and social media posts. (...)
Social Determinants of Health
Extract Social Determinants of Healthcare such as employment, education, social support, housing, financial hardship, substance abuse, demographics, and more. (...)
Calculate Medicare HCC Risk Score
Automatically calculate patient risk adjustment scores, using ICD codes of diseases that are extracted from clinical notes about a patient. (...)
Recommend Available Models for Your Text
This pipeline is used to explore all the available pretrained entity recognition models at once. It recommends which models will provide results on a given document. (...)

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