Healthcare NLP

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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

Source: gradientflow.com

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

Install NLP Software Talk to Us

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

Medical LLMs for:

Q&ARAGExtractSummarize

Sizes:

SML

Quantizations:

q4q8q16

Relation Extraction

Ora

NAME

AGE

cashier

PROFESSION

from

Morocco

LOCATION

Data Enrichment

Content

Medical
Terminologies

SNOMED-CTCPTUMLSICD-10-CMRxNormHPOICD-10-PCSICD-OLOINCMedDRANDCMeSH

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

Scalable to a Cluster

Fast Inference

Hardware Optimized

Community

Peer-Reviewed State-of-the-art Accuracy

Deeper Clinical Document Understanding Using Relation Extraction

New state-of-the-art accuracy on:

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

New state-of-the-art accuracy on:

ADE benchmark
SMM4H benchmark
CADEC entity recognition dataset
CADEC relation extraction dataset

Accurate Clinical and Biomedical Named Entity Recognition at Scale

New state-of-the-art accuracy on:

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

New state-of-the-art accuracy on:

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).