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David Thompson: "Advances in Medical Analytics Solutions for Autonomous Medical Operations on Long-Duration Missions."

Abstract: A review will be presented on the progress made under STMD/Game Changing Development Program Funding towards the development of a Medical Decision Support System for augmenting crew capabilities during long-duration missions, such as Mars Transit. To create an MDSS, initial work requires acquiring images and developing models that analyze and assess the features in such medical biosensor images that support medical assessment of pathologies. For FY17, the project has focused on ultrasound images towards cardiac pathologies: namely, evaluation and assessment of pericardial effusion identification and discrimination from related pneumothorax and even bladder-induced infections that cause inflammation around the heart. This identification is substantially changed due to uncertainty due to conditions of fluid behavior under space-microgravity. This talk will present and discuss the work-to-date in this Project, recognizing conditions under which various machine learning technologies, deep-learning via convolutional neural nets, and statistical learning methods for feature identification and classification can be employed and conditioned to graphical format in preparation for attachment to an inference engine that eventually creates decision support recommendations to remote crew in a triage setting.

Bio: David Thompson began his career with NASA in November, 1975, at the Jet Propulsion Lab in Pasadena, CA. He has a PhD in Planetary Physics at UCLA. He was Member of the Technical Staff in Planetary Physics, carrying out research in Mars Analog Studies related to origin of fluvial features as observed in Viking Images – a task that was focused both on geomorphic feature identification and on math modeling of requisite fluid dynamics that could be responsible for such features. He also focused on nonlinear viscous behavior of the Mars mantle, with modeling related to isostatic rebound and analog analysis due to rebound on Earth from Pleistocene deglaciation. His modeling of fluid features took him to field programs on current surge-glacier systems in Alaska and the Yukon, with eventual modeling of Antarctic Ice Stream Flow, both in the field in Antarctica and with analytics of theoretical modeling of such complex nonlinear flow. He then supported the NASA Climate Program Office at NASA HQ, as well as other Interagency Climate Modeling Initiatives in DC, during 1982-1988. He was appointed Science Advocate for the IOC for Space Station Utilization. David was also named Deputy to the NASA Chief Scientist during this time, and developed several Innovative Research Program Initiatives, as well as guiding the protocol for what are now NASA NRAs.

After being Advanced to Candidacy for a 2nd PhD in Mathematical Logic at Georgetown University, he opted to return to full-time research at NASA Ames in October 1988. He occupied the position of Lead of Applications for the incipient AI Group at Ames, effectively “marrying” AI technologies into requisite missions in both Space and Aero Projects; meanwhile carrying out his own research across a broad spectrum of topics, including early atmospheric modeling and evolution of the primitive Earth, geologic clues for soil sample analysis on Mars Sample Return Missions (as hypothesized at the time), math modeling of Solar Flares and Coronal Mass Ejections leading to Space Weather predictions, signal identification and extraction from galactic background noise for Gravitational Waves for the LISA data challenge, and most recently, for development of Medical Decision Support Systems (utilizing computer-aided ultrasound image analysis, plus statistical learning theory and inference engine development and support) that may augment crew medical capabilities on long-duration space missions, such as Mars Transit. His presentation at this Workshop is about this Medical Decision Support analysis and the Machine Learning methods embodied in the analytics to create advice to remote crew in a medical triage setting.

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