Meet our team at HIMSS 2024 in Orlando, FL, March 11 - 15. Schedule a meeting.
was successfully added to your cart.

Healthcare NLP

State of the Art Medical Language Models

Slider
John Snow Labs is emerging as the clear industry leader for state-of-the-art NLP in healthcare. We cannot recommend a better way to apply the most current, accurate, and scalable technology to your natural language understanding challenges today.
Editor-in-Chief, The Technology Headlines

The most widely used NLP library in Healthcare, by far

NLP Application Case
NLP Application Case
By all accounts, John Snow Labs has created the most accurate software in history to extract facts from unstructured text.
Healthcare Tech Outlook

Make 4-6X Fewer Errors than AWS,
Azure, or GCP

What’s in the Box

Entity Recognition
40 units
DOSAGE
of
insulin glargine
drug
at night
FREQUENCY
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
Suspect diabetes
SNOMED-CT:
473127005
Lisinopril 10 MG
RxNorm:
316151
Hyponatremia
ICD-10:
E87.1
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
BioGPTBioBERTJSL-BERTJSL-sBERTClinicalBERTGloVe-MedT5Flan-T5
Relation Extraction
Ora
NAME
a
25
AGE
yo
cashier
PROFESSION
from
Morocco
LOCATION
Healthcare AI Platform
Data Enrichment
Content
Medical
Terminologies
SNOMED-CTCPTUMLSICD-10-CMRxNormHPOICD-10-PCSICD-OLOINC

2,000+ 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
Core Healthcare Datasets
Scalable to a Cluster
Healthcare Data
Fast Inference
Fast Inference
Hardware Optimized
Hardware Optimized
Community
Community
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

Clinical NLP

See in action

Biomedical NLP

See in action

Healthcare LLM

See 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.
CIO Insights

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).

Veysel Kocaman
Head of Data Science
preloader