An integrated big data framework utilizing stream computing to support real-time clinical decision-making in the field of space medicine

Abstract

Space exploration continues to be one of greatest endeavours of humankind. As manned space exploration extends to deep space, destinations such as the Moon and Mars, technological improvements and scientific advancements are in order, so as to enable safe prolonged human presence in space. Existing challenges of medical care delivery in space need to be addressed, while the meaningful and practical use of the acquired data will enable greater understanding of the impact of space travel on humans. This thesis proposes a novel wholistic approach to the human-technology ecosystem, enabling integration of the various components to address existing challenges of fragmented, retrospective discontinuous file-base data acquisition, in-batch data processing, extensive data down-sampling and an enormous amount of data loss. It presents an innovative solution to support proactive prognostics, diagnostics and health management, while providing the necessary tools to support action-taking and informed decision-making within the field of space medicine.