Demos
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.
By all accounts, John Snow Labs has created the most accurate software in history to extract facts from unstructured text.
Make 4-6X Fewer Errors than AWS,
Azure, or GCP
State-of-the-Art Medical Large Language Models
Clinical Note Summarization
is 30% more accurate than BART, Flan-T5 and Pegasus.
Clinical Entity Recognition
John Snow Labs’ models make half the errors that ChatGPT does.
Extracting ICD-10-CM Codes
is done with a 76% success rate versus 26% for GPT-3.5 and 36% for GPT-4.
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
Language Models
BioGPTBioBERTJSL-BERTJSL-sBERTClinicalBERTGloVe-MedT5Flan-T5
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-OLOINC
1,000+ 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
Healthcare NLP in Action
State Of The Art Accuracy
Production-Grade, Fast & Trainable Implementation of State-of-the-Art Biomedical NLP Research
Deeper Clinical Document Understanding Using Relation Extraction. Hasham Ul Haw, Veysel Kocaman and David Talby, 2022
Mining Adverse Drug Reactions from Unstructured Mediums at Scale. Hasham Ul Haw, Veysel Kocaman and David Talby, 2022
Biomedical Named Entity Recognition at Scale. Authors: Veysel Kocaman and David Talby, 2020
Improving Clinical Document Understanding on COVID-19 Research with Spark NLP. Veysel Kocaman and David Talby, 2021
Accurate Clinical and Biomedical Named Entity Recognition at Scale. Veysel Kocaman and David Talby, 2022
Biomedical Named Entity Recognition in Eight Languages with Zero Code Changes. Veysel Kocaman, Gursev Pirge, Bunyamin Polat, David Talby, 2022
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.
For Data Scientists
Install software, try Python libraries, notebooks, and models on your own infrastructure
