Poster

Presented at OHDSI Europe 2025, Sentinel is a framework that continuously monitors and enhances data quality across OMOP CDM and NLP outputs.

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.

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

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.

Enhancing Hematologic Oncology Data Capture in the OMOP CDM: Methodological Advances and Challenges

Learn more on the results of a multi-center study on cardiovascular toxicities in 1500+ real-world cancer patients treated with immune checkpoint inhibitors

Discover the initial findings of a multi-center study into the real-world usage and adverse events of immune checkpoint inhibitors.

Analysis of Lung Cancer Patient Treatment with Immune Checkpoint Inhibitors Using Natural Language Processing for Data Extraction from Electronic Health Records

Discover the initial findings of the ICI-treated lung cancer patient cohort of 730+ patients regarding demographic and clinical characteristics, ICI treatments, and overall survival (OS).

To bridge the gap between clinical trial patients and real-world populations, we conducted a comprehensive study in Belgium to characterize cancer patients treated with immune checkpoint inhibitors (ICIs), which have demonstrated survival advantages in various cancer types.

Accessing and standardizing raw clinical data across multiple hospitals presents a challenge in Oncology. However, it is crucial to use real-world data sources such as electronic health records (EHR) to leverage untapped information. We are building a federated data network to facilitate GDPR-compliant data exchange of large datasets, with hospitals as owners. This network, governed by a common data model (CDM), is aimed at fostering multicenter, observational, real-world evidence (RWE) studies in Oncology, with breast cancer, lung cancer, and immunotherapy as therapeutic areas of focus.

NLP algorithms allow rapid data extraction from pathology reports, thereby offering a time-efficient and cost-effective alternative to manual data processing. In turn, this approach enables rapid insight in current biomarker testing rates and prevalence of (actionable) mutations.

The study aimed to analyze individuals with ATTR-CM in a real-world heart failure patient population using a federated OMOP-CDM database generated from data of electronic health records.