Knowledge Center

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).

In collaboration with Heart Center Aalst, we conducted a study in which complete Electronic Health Records of patients were processed and analyzed using LynxCare’s cutting-edge clinical NLP-AI technology in search of phenotypes compatible with aTTR-CM, a rare cardiac disease.

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.

This study aimed to investigate the prevalence of impaired renal function at the time of Heart Failure diagnosis and its correlation with short and long-term outcomes in a real-world cohort of Heart Failure patients.

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.

Information held in Electronic Health Records (EHRs) hold a significant opportunity to provide physicians and researchers with better and more insights to improve disease management and treatment.

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.

RWE is becoming a valuable addition to randomized controlled clinical trials to better understand the mechanisms and outcomes of cancer treatments. However, most RWD is locked in a non-uniform patient reporting system by hospitals and healthcare professionals. Download our use case in immuno-oncology by completing the form and read how artificial intelligence can help unlock real-world evidence in immunotherapy.