Publication

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

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

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

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

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.

An example of the power of real-world data, technology and collaboration to transform patient care. The study aimed to shed light on patient eligibility for SGLT2 inhibitors as a therapy for heart failure patients, providing valuable information for clinicians and researchers.

A large multi-country Real-World Evidence study in heart failure shows that 1–2% of the contemporary adult population has heart failure. Through analyzing data from both national registries and Electronic Health Records, further insights into the burden of heart failure on patients and on our healthcare systems can be gained.

A clinically-validated NLP algorithm offers a valid and accurate tool to detect red flag symptoms in medical records across multiple disciplines, supporting better screening for patients with rare diseases.

LynxCare’s data insights enabled the detection of unknown transient events associated with direct anterior approach total hip arthroplasty.