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European Health Data Space

Accelerating Clinical Research: Using Big Data to Fast-Track Patient Selection

Selecting patients for clinical studies can be a slow and tedious process. But it does not have to be. Emerging technologies are paving the way for faster and more efficient patient selection, and our Clinical REsearch Patient Selection (CREPS) project in collaboration with a renowned Belgian hospital is leading the change. In this blog post, we will dive into the world of real-world evidence (RWE) and explore how the use of big data and Natural Language Processing (NLP) can revolutionize the way we screen and select patients for clinical studies while always remaining 100% GDPR compliant. Let us explore how LynxCare’s technology can speed up and simplify your trials’ patient selection process.

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The potential of Natural Language Processing in cardiology is becoming increasingly apparent

A metanalysis published in the Heart journal identified 37 studies developing and applying NLP in various areas of cardiology. This systematic review found that the majority of NLP work in cardiology has been concentrated in a small number of clinical domains and NLP methods, with the most common applications being used for disease identification and classification purposes. Patient sample sizes ranged from 60 to 621,000. Data sources ranged from single centers to national healthcare system databases and registries, most of them taking place in the US.

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Preparing French hospitals for France 2030: building your health data warehouse (i.e. ‘EDS’)

LynxCare can help French hospitals to make the most of all their clinical data by building OMOP data warehouses (i.e. ‘EDS’) to improve patient care and research.

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Heart Failure Awareness Week - Detect the Undetected

As we begin 2023’s Heart Failure Awareness Week, it is important to reflect on how we can better detect and treat this condition. This year's theme, "Detect the Undetected" highlights the need for early detection and intervention to improve patient outcomes.

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Decoding the Genetic Secrets of Lung Cancer with the help of technology

Lung cancer claims millions of lives worldwide every year. And there’s no single cure. But what if we could gain deeper insights into the genetic makeup of a population of 30,000+ lung cancer patients? Thanks to our Natural Language Processing (NLP) algorithm and a team of dedicated researchers, we now have a clearer picture of the non-small cell lung cancer (NSCLC) patient population and the specific genetic mutations that drive tumor growth. In this blog, we’ll share our AI-driven findings from the Dutch nationwide registry of histo- and cyto-pathology (PALGA) registry. Read on to discover how AI data processing with 95.9% accuracy can propel the progress of cancer treatment. ‍

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LynxCare is proud to be supporting the Muco association at the Antwerp 10 Miles

Muco is an association by and for people with cystic fibrosis and their families. Together with families, muco centers and healthcare providers, the association is committed to ensuring a better, longer and happier life for all muco patients.

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Effortless data entry: using AI to automate the completion of a national (gastrointestinal) cancer registry

Cancer research is the most rapidly advancing field of medical science – largely driven by the emergence of accessible and high-quality clinical data. LynxCare's NLP algorithm is benefiting gastrointestinal (GI) cancer research by offering new insights into this group of complicated diseases. In this article you’ll learn how this cutting-edge technology has enabled the mapping of unstructured data from over 24,500 health records, with precision and recall rates reaching 94%. We’ll highlight the unique capabilities of AI in unlocking the potential of real-world evidence (RWE), including examples from our use case for esophageal, pancreatic, and gastric cancers.

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Revolution in Rare Diseases: how technology can support the more rapid diagnosis of rare diseases

Rare diseases, such as hATTR polyneuropathy, are a major diagnostic challenge. The biggest obstacle? The lack of specific symptoms in hATTR polyneuropathy, which doesn’t allow for its early diagnosis, lowering the patient’s chances of getting timely access to the right treatment. Read on to learn how an AI-driven framework using Natural Language Processing (NLP) can detect hATTR red flag symptoms in Electronic Health Records (EHRs), leading to a 48.6% relative increase in genetic testing for this condition.

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The European Health Data Space: an opportunity to improve health for all.

In May 2022, the European Commission announced the launch of the European Health Data Space (EHDS), one of the central building blocks of a strong European Health Union. The EHDS will help the EU to achieve a quantum leap forward in the way healthcare is provided to people across Europe.

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