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State of the Art Clinical Data Curation

Turn unstructured clinical notes into structured patient registries, cohorts, and quality measures. Our healthcare-specific language models deliver document-level and patient-level data curation with regulatory-grade accuracy.

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Trusted by Leading Healthcare Organizations

Leveraging Healthcare NLP Models in Regulatory Grade Oncology Data Curation

Advancing drug safety research by extracting real-world data from EHR clinical notes using NLP and machine learning

Identifying mental health concerns, subtypes, temporal patterns, and differential risks among children with Cerebral Palsy using NLP on EHR data

Using Generative AI for Data Extraction Clinical Support

Identifying Housing Insecurity and Other Social Determinants of Health from Free-Text Notes

Transforming 200M+ clinical notes into research-ready data with de-identification, extraction, and coding at enterprise scale

State-of-the-Art Accuracy

Performance metrics validated on industry benchmarks and real-world clinical data

>99%

De-identification
Accuracy

>95%

Oncology NER F1 Score

>92%

SDOH Extraction
Accuracy

3000+

Pre-trained Models

Validated by leading institutions: Our models have been benchmarked against public datasets (n2c2, i2b2) and validated in production deployments at Mayo Clinic, Stanford Medicine, and Roche Diagnostics.

We Train Our Own Language Models, Purpose-Built for Healthcare

Unlike general-purpose LLMs, our models are trained exclusively on clinical data for
healthcare-specific accuracy and compliance

Fewer Errors, Higher Accuracy

Healthcare-specific training from the ground up means our models understand medical context, abbreviations, and clinical patterns that general LLMs miss. No hallucinations on critical medical data.

Deterministic & Reproducible

Unlike probabilistic general-purpose LLMs, our models deliver consistent, reproducible results every time - essential for reproducible compliance and downstream analytics.

Runs in Your Private Environment

Deploy on-premise, in your VPC, or air-gapped environments. No data leaves your infrastructure. Full HIPAA and GDPR compliance with complete data sovereignty

Cheaper Inference, No GPU Required

Our efficient models run on commodity hardware with no GPU required for inference. Process millions of documents at a fraction of the cost of API-based LLMs.

Patient-Level Reasoning & Longitudinal Analysis

Build comprehensive patient timelines across multiple encounters. Track disease progression, treatment responses, and outcomes over time - essential for cancer registries, clinical trials, and longitudinal research.

Specialty-Specific Models, Out of the Box

Each medical specialty gets dedicated models trained on domain-specific data. Deploy production-ready oncology, cardiology, or mental health models immediately without custom training.

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Oncology models that extract the full cancer journey

Extract 40+ specialized oncology entities, from tumor classifications to treatment regimens, with regulatory-grade accuracy.

Capturing the complete cancer journey from diagnosis to outcome:

Histological types and tumor classifications
Cancer staging (TNM, pathological, clinical)
Biomarkers and genetic mutations
Treatment regimens and chemotherapy cycles
Diagnostic tests and procedures
Metastasis sites and progression events

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Leveraging healthcare NLP models in regulatory-grade oncology data curation to enhance abstraction efficiency and data quality.

From 1.4M+ physician notes with NLP and LLMs, achieving 93% F1 score for entity extraction and enabling clinical trial matching.

Ronin: causality-aware clinical reasoning for personalized cancer treatment, with transparent patient-specific outputs.

Building oncology patient timelines and matching patients with NCCN clinical guidelines using healthcare-specific LLMs.

Mental Health & Behavioral Analytics

Extract critical mental health indicators to improve patient safety, risk assessment, and care outcomes.

Capture 50+ specialized entities from psychiatric notes, therapy sessions, and behavioral health records, including:

Diagnoses & Conditions: Depression, anxiety, PTSD, bipolar disorder, schizophrenia, OCD, eating disorders
Risk Indicators: Suicide ideation, self-harm behaviors, violence risk, substance abuse patterns
Symptoms & Behaviors: Mood changes, sleep disturbances, hallucinations, delusions, panic attacks
Treatment & Response: Medications, therapy types, treatment adherence, clinical outcomes
Social Factors: Family history, trauma exposure, social support, housing stability

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Identifying opioid-related adverse events from unstructured text in electronic health records using rule-based algorithms and deep learning methods.

Advancing drug safety research for the FDA (MOSAIC-NLP project) by extracting real-world data from EHR clinical notes using NLP and machine learning.

Identifying emotional states and stressors of patients at different stages of disease journeys by analyzing social media conversations using NLP to uncover patient challenges and improve care strategies.

Identifying specific mental health phenotypes and risk factors in children with cerebral palsy by extracting 80+ clinical entities from 50 million EHR notes using NLP.

Social Determinants of Health (SDOH)

Identify social factors impacting health outcomes, readmissions, and care equity.

Our models detect 40+ social determinant categories:

Housing stability and homelessness
Food insecurity and nutrition access
Transportation barriers
Financial hardship and employment status
Education level and health literacy
Social support networks and isolation
Violence, abuse, and trauma exposure
Access to care and insurance status

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Published Research & Validation

Our SDOH models have been validated in peer-reviewed research and deployed in production healthcare systems

Extracted 60,717 SDOH factors from 13,258 oncology documents with 0.88 F1 score accuracy, outperforming benchmark models by 5% in extracting 13 SDOH categories.

Population health analytics for veterans using SDOH extraction to reduce emergency visits and improve care coordination.

Identifying housing insecurity and social determinants from free-text clinical notes with >90% F1 score.

Advanced Medical Coding & Terminology Mapping

100+ pre-trained entity resolution models that map clinical concepts to standard terminologies accurately and consistently.

Our specialized models map clinical concepts to 15+ standard medical terminologies:

ICD-10-CM: Diagnoses and conditions
SNOMED-CT: Clinical findings and procedures
RxNorm: Medications and drug classes
CPT: Clinical procedures
LOINC: Laboratory tests and observations
NDC: National Drug Codes
MedDRA: Adverse events and safety reporting
UMLS CUI: Unified medical concepts
HPO: Human phenotype ontology
MeSH: Biomedical literature indexing
ICD-O: Oncology morphology and topology

View Entity Resolution Models

Try It Yourself

Explore 300+ live demos and interactive notebooks

Oncology

Extract cancer types, staging, biomarkers, and treatments from pathology reports and clinical notes.

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Mental Health

Identify psychiatric diagnoses, substance use, and mental health indicators from clinical documentation.

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Social Determinants

Extract housing, employment, education, and social support information from free-text notes.

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Diagnoses & Procedures

Identify diagnoses, procedures, symptoms, and their assertion status (present, absent, uncertain) from clinical documents.

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Radiology

Extract anatomical locations, imaging findings, and observations from radiology reports with assertion status.

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Drugs & Adverse Events

Identify medications, dosages, adverse drug reactions, and drug-event relationships from clinical text.

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Labs, Tests & Vitals

Detect laboratory results, vital signs, test names, and clinical measurements from medical records.

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Medical Coding

Map clinical entities to ICD-10, SNOMED CT, RxNorm, and other standard terminologies.

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Risk Factors & HCC

Calculate Medicare HCC risk scores and identify clinical risk factors for value-based care programs.

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Ready to Transform Your Clinical Data?

Schedule a personalized demo to learn how you can deploy best-in-class automated clinical data abstraction at scale.

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