Knowledge Center

We evaluated the performance of LynxCare’s clinical NLP pipeline against two open-source alternatives. Our goal was to assess out-of-the-box performance on multilingual biomedical text, particularly electronic health record (EHR) data.

Presented at ELCC 2025, the first-of-its-kind Belgian study harnesses AI and NLP to provide real-world insights into lung cancer patient trajectories, bridging the gap between clinical trial results and routine clinical practice.

Our joint pan-tumour study on cardiovascular toxicities in cancer patients treated with immune checkpoint inhibitors just got published in ESMO Real World Data and Digital Oncology.

Introducing LynxCare Sentinel: Your Framework for Data Quality Excellence! With Sentinel, hospitals can unlock the true potential of their data, enabling AI-driven insights, cutting-edge research, and innovation powered by the OMOP Common Data Model (OHDSI). It’s time to elevate data quality, drive progress, and truly harness the power of health data for better patient outcomes! Watch the demo to see how Sentinel works and how it can transform healthcare data quality.

This real-world evidence study highlights the potential of integrating eGFR trends into clinical assessments, paving the way for more personalized and proactive HF management.

This study presents performance insights into our clinical Natural Language Processing (NLP) pipeline. We have developed multilingual transformer-based models, trained on in-house curated data, for concept recognition, normalization and attribute extraction such as negation and temporality

Another powerful example of how LynxCare's precision data is driving collaborative scientific breakthroughs in oncology.

Learn first-hand how oncologists are generating collaborative scientific value through the granular precision data unlocked by LynxCare.

Cardiovascular adverse events in cancer patients receiving immune checkpoint inhibitors are often under-detected in clinical trials, partly due to the strict inclusion criteria and limited follow-up typical of such studies. This underscores the need for real-world data analysis to gain a more comprehensive understanding of these events.