The Big Data Dilemma

    A database linking medical history and tissue compound response has direct relevance to future drug discovery projects and healthcare stratification. By making this data available to point-of-care centers, it could transform medication management approaches and improve individual patient care[1]. As the concept of integrating Big Data with healthcare is on the rise, it is increasingly appropriate to have reliable information at your fingertips.

    REPROCELL has initiated a research and development project where historical data regarding Inflammatory Bowel Disease (IBD) has been collated. Tissue samples were donated by patients for whom conservative treatment had failed and subsequently required surgical intervention. This data collection project is in its infancy, but it already includes the anonymized medical history of over 250 different donors.

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    Machine Learning and Big Data in Precision Medicine

    Healthcare data is predicted to expand by 43 percent by 2020, to an incomprehensible level of 2.3 zettabytes. The size of the data is also not the only inevitable issue, it’s the type of data. Eighty percent of it is completely unstructured and mostly unlabelled, meaning organizations will find it increasingly difficult to extract any value or outcomes from the datasets [1].

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