Medical Diagnosis During Space Tourism And Future Mars Colonization

Aravindhan, A; Laxmikanth, G; S, Shanmuga Priya; Kamalraj, S.

doi:10.1109/icaccs48705.2020.9074349

Cited by 6 publications

(5 citation statements)

References 6 publications

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“…We discovered several topics linked to this parameter by reviewing the Scopus academic articles that related to it. For example, Aravindhan et al [133] discussed the importance of medical examinations during space travel for successful survival and the difficult performance of human exploration throughout space flight. It consists of training, selecting spaceflight, and recovery from several side effects post-flight.…”

Section: Space Tourismmentioning

confidence: 99%

Smarter Sustainable Tourism: Data-Driven Multi-Perspective Parameter Discovery for Autonomous Design and Operations

2023

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Global natural and manmade events are exposing the fragility of the tourism industry and its impact on the global economy. Prior to the COVID-19 pandemic, tourism contributed 10.3% to the global GDP and employed 333 million people but saw a significant decline due to the pandemic. Sustainable and smart tourism requires collaboration from all stakeholders and a comprehensive understanding of global and local issues to drive responsible and innovative growth in the sector. This paper presents an approach for leveraging big data and deep learning to discover holistic, multi-perspective (e.g., local, cultural, national, and international), and objective information on a subject. Specifically, we develop a machine learning pipeline to extract parameters from the academic literature and public opinions on Twitter, providing a unique and comprehensive view of the industry from both academic and public perspectives. The academic-view dataset was created from the Scopus database and contains 156,759 research articles from 2000 to 2022, which were modelled to identify 33 distinct parameters in 4 categories: Tourism Types, Planning, Challenges, and Media and Technologies. A Twitter dataset of 485,813 tweets was collected over 18 months from March 2021 to August 2022 to showcase the public perception of tourism in Saudi Arabia, which was modelled to reveal 13 parameters categorized into two broader sets: Tourist Attractions and Tourism Services. The paper also presents a comprehensive knowledge structure and literature review of the tourism sector based on over 250 research articles. Discovering system parameters are required to embed autonomous capabilities in systems and for decision-making and problem-solving during system design and operations. The work presented in this paper has significant theoretical and practical implications in that it improves AI-based information discovery by extending the use of scientific literature, Twitter, and other sources for autonomous, holistic, dynamic optimizations of systems, promoting novel research in the tourism sector and contributing to the development of smart and sustainable societies.

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Section: Space Tourismmentioning

confidence: 99%

Smarter Sustainable Tourism: Data-Driven Multi-Perspective Parameter Discovery for Autonomous Design and Operations

2023

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“…We discovered several topics linked to this parameter by reviewing the Scopus academic articles that related to it. For example, Aravindhan et al [121] discussed the importance of medical examinations during space travel for successful survival and the difficult performance of human exploration throughout space flight. It consists of training, selecting spaceflight, and recovery from several side effects after post-flight.…”

Section: Space Tourismmentioning

confidence: 99%

Smarter Sustainable Tourism: Data-Driven Multi-Perspective Parameter Discovery for Autonomous Design and Operations

Alsahafi¹,

Alzahrani²,

Mehmood³

2023

Preprint

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The Global natural and manmade events are exposing the fragility of the tourism industry and its impact on the global economy. Prior to the COVID-19 pandemic, tourism contributed 10.3% to the global GDP and employed 333 million people but saw a significant decline due to the pandemic. Sustainable and smart tourism requires collaboration from all stakeholders and a comprehensive understanding of global and local issues to drive responsible and innovative growth in the sector. This paper presents an approach for leveraging big data and deep learning to dis-cover holistic, multi-perspective (e.g., local, cultural, national, and international) and objective information on a subject. Specifically, we develop a machine learning pipeline to extract parameters from academic literature and public opinions on Twitter, providing a unique and comprehensive view of the industry from both academic and public perspectives. The academic-view dataset was created from the Scopus database and contains 156,759 research articles from 2000 to 2022, which were modelled to identify 33 distinct parameters in 4 categories: Tourism Types, Planning, Challenges, and Media &amp; Technologies. A Twitter dataset of 485,813 tweets was collected over 18 months starting March 2021 to August 2022 to showcase public perception of tourism in Saudi Arabia, which was modelled to reveal 13 parameters categorized into two broader sets: Tourist Attractions and Tourism Services. Discovering system parameters are re-quired to embed autonomous capabilities in systems and for decision-making and problem-solving during system design and operations. The proposed approach improves AI-based information discovery by extending the use of scientific literature, Twitter, and other sources for autonomous, dynamic optimizations of systems, promoting novel research in the tourism sector and contributing to the development of smart and sustainable societies. The paper also presents a comprehensive knowledge structure and literature review of the tourism sector based on over 250 research articles.

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“…Although, this solution is feasible for LEO missions, long latency in exploration and planetary missions will require autonomous operation onboard. Aravindhan et al proposed in [21] a Raspberry Pi solution for online health diagnosis to operate during space tourism and future Mars colonisation by illustrating its potential application to fundoscopies when suspicion of visual impairment due to intracranial pressure.…”

Section: State Of the Artmentioning

confidence: 99%

Basic principles and concept design of a real-time clinical decision support system for managing medical emergencies on missions to Mars

García‐Gómez¹

2020

Preprint

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Space agencies and private companies prepare the beginning of the human space exploration for the 2030s with missions to put the first human on the Mars surface. The absence of gravity and radiation, along with distance, isolation and hostile environment are expected to increase medical events with unidentified manifestations along the crewmembers. The current healthcare strategy based on telemedicine and the possibility to stabilise and transport the injured crewmember to a terrestrial definitive medical facility is not applicable in exploration class missions. Therefore, full autonomous capability to solve medical situations will guide design of future healthcare systems onboard.This study presents the basic principles and concept design of a software suite to bring on-board decision support to help dealing with medical conditions of the crewmembers, with special attention to emergency care situations and critical monitoring. MEDEA is an autonomous clinical decision support system that provides real-time advice to tertiary interventions on-board in space exploration missions. Its basic principles are 1) to give real-time support for medical decision making, 2) give patient-specific advice for executive problemsolving, 3) take into account available information from life support and monitoring of crewmembers, 4) be full autonomous from remote facilities, 5) continuously adapt predictions to physiological disturbance and changing conditions, 6) optimise medical decision making in terms of mission fundamental priorities, 7) take into account medical supplies and equipment on-board, 8) apply health standards for levels of care V, 9) apply ethical standards for spaceflights, and 10) apply ethical standards for artificial intelligence.To fulfil these principles, MEDEA is conceptually designed as a software suite consists of four interconnected modules. The main of them is responsible to give direct advice to the crew by means of a deep learning multitask neural network to predict the characters of the medical event (e.g. life-threatening, delayability, ethical dilemma, duration of therapy, and compatible diagnoses), a classifier of the tertiary medical intervention and an optimiser of medical action plans. This module is continuously evaluate and re-trained with changing physiological data from the crew by an adaptive deep learning module, ensuring fairness, interpretability and traceability of decision making during the full operational time of MEDEA. Finally, MEDEA would be semantically interoperable with health information systems on-board by a FHIR module.The deployment of MEDEA on-board of future missions to Mars will facilitate the deployment of a comprehensive preventive medical strategy. Moreover, the advance in technology may represent a stepstone of the future quantitative medicine on Earth and on the expansion of humans throughout the solar system.

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Medical Diagnosis During Space Tourism And Future Mars Colonization

Cited by 6 publications

References 6 publications

Smarter Sustainable Tourism: Data-Driven Multi-Perspective Parameter Discovery for Autonomous Design and Operations

Smarter Sustainable Tourism: Data-Driven Multi-Perspective Parameter Discovery for Autonomous Design and Operations

Smarter Sustainable Tourism: Data-Driven Multi-Perspective Parameter Discovery for Autonomous Design and Operations

Basic principles and concept design of a real-time clinical decision support system for managing medical emergencies on missions to Mars

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