Demos
John Snow Labs is the De-facto Industry Leader for Medical Large Language Models
By all accounts, John Snow Labs has created the most accurate software in history to extract facts from unstructured text.
What’s in the Box
Entity Recognition
EGFR
Biomarker
positive Result
invasive
HISTOLOGICAL_TYPE
adenocarcinoma
Diagnosis
classified as T1bN1M0
Staging
De-Identification
Algorithms
Information Extraction
- Document Classification
- Entity Disambiguation
- Contextual Parsing
- Patient Risk Scoring
Clinical Grammar
- Deep Sentence Detector
- Medical Spell Checking
- Medical Part of Speech
- Terminology Mapping
Entity Linking
Abdominal pain
ICD10CM:
R10.84
Dermatitis
MedDRA
10012431
Hernia repair
SNOMED:
50465008
Question Answering
Algorithms
Data Obfuscation
- Name Consistency
- Gender Consistency
- Age Group Consistency
- Format Consistency
Zero-Shot Learning
- Entities by Prompt
- Relations by Prompt
- Classification by Prompt
- Relative Data Extraction
Assertion Status
Fever and sore throat
PRESENT
No stomach pain
ABSENT
Father with Alzheimer
FAMILY
Summarization
Content
Medical
Language Models
Language Models
Medical LLMs for:
Q&ARAGExtractSummarize
Sizes:
SML
Quantizations:
q4q8q16
Relation Extraction
Ora
NAME
a
25
AGE
yo
cashier
PROFESSION
from
Morocco
LOCATION
Data Enrichment
Content
Medical
Terminologies
Terminologies
SNOMED-CTCPTUMLSICD-10-CMRxNormHPOICD-10-PCSICD-OLOINCMedDRANDCMeSH
2,500+ Pretrained Models
Clinical Text
Signs, Symptoms, Treatments, Findings, Procedures, Drugs, Tests, Labs, Vitals, Sections, Adverse Effects, Risk Factors, Anatomy, Social Determinants, Vaccines, Demographics, Sensitive Data
Biomedical Text
Clinical Trial Design, Protocols, Objectives, Results; Research Summary & Outcomes; Organs, Cell Lines, Organisms, Tissues, Genes, Variants, Expressions, Chemicals, Phenotypes, Proteins, Pathogens
Trainable & Tunable
Scalable to a Cluster
Fast Inference
Hardware Optimized
Community
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
Some Al companies stand out via outstanding academic validation; some via successful customers and deployments; and yet others by using Al for good. John Snow Labs is utterly unique in going all three.
Solving Healthcare NLP Problems at Scale
Current State-of-the-Art Accuracy for Key Medical Natural Language Processing Benchmarks
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).
