Articles tagged: Clinical

  1. Unlocking important RWE from patient data (Part 3) – Can we find all the relevant patients?

    Image and link to LinkedIn profile of blog author Arvind Swaminathan

    In our final installment of this series, we demonstrate how to extract a relevant subset of patients from the simulated data using two approaches – one using SciBite tools and one without.

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  2. Unlocking important real world evidence from patient data (Part 2) – Data domain deep dive

    Image and link to LinkedIn profile of blog author Arvind Swaminathan

    In this part of our blog series, "Unlocking important real-world evidence from patient data," we will demonstrate our expertise in various important data domains using SciBite tooling, including problem list diagnoses, lab orders, and medication orders.

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  3. Unlocking important RWE from patient data (Part 1) – Why and how?

    Image and link to LinkedIn profile of blog author Arvind Swaminathan

    In this three-part blog series, we explore the challenges healthcare organizations face in unlocking patient data for real-world evidence. In part 1 Unlocking Important Real World Evidence (RWE) from Patient Data – Why and How?

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  4. Matching patients to clinical trials
     

    Patient X, suffering from an untreatable gastrointestinal disease, chats with a large language model for advice. GPT suggests looking at clinical trials and Patient X finds 10 active recruiting trials but is unsure which to choose. Patient X consults his doctor, who recommends a trial from a pharmaceutical company. What could go wrong?

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  5. Healthcare digital transformation challenges: Can we enable healthcare systems to trust their data?

    Image and link to LinkedIn profile of blog author Arvind Swaminathan

    At SciBite, we are passionate about enabling organizations to make full use of their data to help them make evidence-based decisions, especially to help organizations overcome their healthcare digital transformation challenges. To support organizations on this journey, we offer a suite of products to help organizations adopt FAIR data standards.

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  6. Delivery of precision medicine through alignment of clinical data to ontologies

    Precision medicine is changing the way that we think about the treatment of disease, moving from broad-acting therapies to therapies tailored to the individual patient. This increasingly relies on real-world data (RWD), encompassing a diverse range of sources, spanning multi-omic molecular characterisation of the patient’s condition, clinical presentation, treatment, and broader medical histories.

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  7. Exploring breast cancer biomarkers with a literature biomarker database

    In this piece we'll show how natural language processing can be applied to build a searchable database of disease biomarkers, presented in the context of their corresponding scientific publications. To illustrate the power of this approach we'll focus on examples of protein biomarkers relating to Breast Cancer.

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  8. Unlocking patents as a data source in the Life Sciences

    Throughout this blog we highlight some complexities that exist in extracting meaningful information from patents and show various solutions, making use of SciBite technology alone or, augmented by or delivered by our partners.

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  9. Using scientific data technology to access the emerging trends in immuno-oncology research

    In this blog, we explain more about our Immunonc vocabulary, comprising the concepts relating to this field, which enables our customers to be better informed about the current advances and emerging trends in cancer immunotherapy research, keeping them at the forefront of cancer treatment.

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  10. The pivotal role of semantic enrichment in the evolution of data commons

    In this blog post, discover how Pfizer have integrated SciBite’s semantically enriched vocabularies into their Data Commons project, which has the goal of enabling scientists to develop and refine hypotheses by investigating correlations between genetic and phenotypic data.

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  11. Text mining drug labels for genetic factors influencing efficacy and safety

    Only 50-75% of patients respond beneficially to first-line drug therapy, causing unnecessary costs to healthcare providers and, more importantly, adversely impacting a patient’s quality of life. Text mining drug labels for genetic factors influencing efficacy and safety to support clinicians at the point of prescription.

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