The most widely used NLP library in Healthcare, by far
What’s in the Box
- Document Classification
- Entity Disambiguation
- Contextual Parsing
- Patient Risk Scoring
- Deep Sentence Detector
- Medical Spell Checking
- Medical Part of Speech
- Terminology Mapping
- Name Consistency
- Gender Consistency
- Age Group Consistency
- Format Consistency
- Entities by Prompt
- Relations by Prompt
- Classification by Prompt
- Relative Data Extraction
Language Models
Terminologies
2,000+ Pretrained Models
Signs, Symptoms, Treatments, Findings, Procedures, Drugs, Tests, Labs, Vitals, Sections, Adverse Effects, Risk Factors, Anatomy, Social Determinants, Vaccines, Demographics, Sensitive Data
Clinical Trial Design, Protocols, Objectives, Results; Research Summary & Outcomes; Organs, Cell Lines, Organisms, Tissues, Genes, Variants, Expressions, Chemicals, Phenotypes, Proteins, Pathogens
Peer-Reviewed State-of-the-art Accuracy
Deeper Clinical Document Understanding Using Relation Extraction
- 2019 Phenotype-Gene Relations dataset
- 2018 n2c2 Posology Relations dataset
- 2012 Adverse Drug Events Drug-Reaction dataset
- 2012 i2b2 Clinical Temporal Relations challenge
- 2010 i2b2 Clinical Relations challenge
Mining Adverse Drug Reactions from Unstructured Mediums at Scale
- ADE benchmark
- SMM4H benchmark
- CADEC entity recognition dataset
- CADEC relation extraction dataset
Accurate Clinical and Biomedical Named Entity Recognition at Scale
- 2018 n2c2 medication extraction
- 2014 n2c2 de-identification
- 2010 i2b2/VA clinical concept extraction
- 8 different Biomedical NLP benchmarks
Biomedical Named Entity Recognition in Eight Languages with Zero Code Changes
- LivingNER dataset using a single model architecture in English, French, Italian, Portuguese, Galatian, Catalan & Romanian
Healthcare NLP in Action
Solving Healthcare NLP Problems at Scale
Being the most widely used library in the healthcare industry, John Snow Labs’ Healthcare NLP comes with 2,000+ pretrained models that are all developed & trained with latest state-of-the-art algorithms to solve real world problems in the healthcare domain at scale. To provide reliable models and tools all the time while covering edge cases in real-world data and improve how well models generalize, the datasets and models are updated and augmented on a regular basis.
This talk shares accuracy benchmarks from the healthcare-specific models on De-Identification, Named Entity Recognition and Entity Resolution Models. It compares accuracy with respect to both peer-reviewed academic benchmarks and the commercial solutions provided by major cloud providers (AWS Medical Comprehend, GCP Healthcare API and Azure Text Analytics for Health).