Advancing Health Care with Big data and Machine Learning: Case studies and challenges

September 4, 2020 4:15 - 5:45

Location: Virtual Room 2


Recent technological leaps have prompted an unprecedented volume of available biological and medical data, pleading the development of new ways to store, integrate and analyse millions of data points, for high definition of health and disease processes. The advent of BIG data, coupled with the possibilities offered by machine learning (ML) and AI, are poised to transform medicine (and healthcare). ML offers advantages for processing large amounts of complex data to improve diagnostics, predict outcomes and tailor treatments for a personalized care. Researchers continuously improve the models used for analysing aggregated data from multiple layers for what constitutes a disease: DNA, proteins, organs, organisms, populations.
The EU funded, intergovernmental programme, COST, supports pan-European research networks (Actions) addressing this topic: GEMSTONE brings the knowledge arising from high-throughput analysis of available big data in musculoskeletal biology and enables additional genomic discoveries, aiming at translating those into clinical applications. ML4Microbiome links microbiome researchers and data-driven ML experts, facilitating the identification of predictive and discriminatory ‘omics’ features, for unlocking its clinical and scientific potential. NeuralArchCon addresses the relation between neural architecture and consciousness using advanced statistical modelling, also ML, to form a data driven neuroarchitectural model of consciousness and improve the predictive accuracy of prognoses for disorders of consciousness. LEGEND addresses how to manage health issues in children with leukaemia/lymphoma with rare cancer predisposition syndromes, supported by new technologies allowing high-throughput genome-wide genotyping, and the availability of biospecimens from large patient cohorts. Members of these Actions will present recent developments and discuss the advantages and challenges of processing health BIG data using advanced analysis and ML techniques."

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