Scalable data processing through advanced clinical NLP

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EHR-derived insights with LynxCare Clinical NLP

OMOP concept

Entity Linking

Attribute Extraction

Relation Extraction

Named Entity Recognition (NER)

Extracting data from clinical notes at scale

Data curation in action

In today’s healthcare landscape, accurate and efficient data processing is critical. LynxCare’s proprietary disease-specific data mining models can revolutionize this process. To highlight the full potential of our Natural Language Processing (NLP) technology, explore our demo that showcases how our technology performs across multiple languages in diverse healthcare contexts.

Trained on 50M+ patient records, disease-specific

Multilingual performance

Structured datesets get enriched powered by multilingual transformer-based NLP models.

Data Quality checked

Quality checks are performed, i.e. recall and precision check, to verify if the data being picked up is both complete and correct.

Read our technical research paper on how our proprietary pipeline compares to Large Language Models

Improved precision

Closing evidence gaps with deep, granular clinical data

What percentage of immuno-onco patients have auto-immune disease?

EHR-structured data only

What percentage of lung cancer patients have COPD?

EHR-structured data only

What percentage of atrial fibrillation patients have diabetes?

EHR-structured data only

*The percentages are estimates based on real-world datasets within the LynxCare Hospital Network.

Data Quality

Under the evolving European Health Data Space (EHDS) guidelines, the quality and reliability of electronic health record (EHR) data are critical for advancing AI-driven decision tools and clinical research. LynxCare’s Sentinel is a robust, OMOP Common Data Model-based system designed to continuously measure, benchmark, and improve EHR-derived datasets for regulatory compliance, collaborative studies, and translational research.