Biomonitoring and precision health in deep space supported by artificial intelligence

Ryan T. Scott; Lauren M. Sanders; Erik L. Antonsen; Jaden J.A. Hastings; Seung min Park; Graham Mackintosh; Robert J. Reynolds; Adrienne L. Hoarfrost; Aenor Sawyer; Casey S. Greene; Benjamin S. Glicksberg; Corey A. Theriot; Daniel C. Berrios; Jack Miller; Joel Babdor; Richard Barker; Sergio E. Baranzini; Afshin Beheshti; Stuart Chalk; Guillermo M. Delgado-Aparicio; Melissa Haendel; Arif A. Hamid; Philip Heller; Daniel Jamieson; Katelyn J. Jarvis; John Kalantari; Kia Khezeli; Svetlana V. Komarova; Matthieu Komorowski; Prachi Kothiyal; Ashish Mahabal; Uri Manor; Hector Garcia Martin; Christopher E. Mason; Mona Matar; George I. Mias; Jerry G. Myers; Charlotte Nelson; Jonathan Oribello; Patricia Parsons-Wingerter; R. K. Prabhu; Amina Ann Qutub; Jon Rask; Amanda Saravia-Butler; Suchi Saria; Nitin Kumar Singh; Michael Snyder; Frank Soboczenski; Karthik Soman; David Van Valen; Kasthuri Venkateswaran; Liz Warren; Liz Worthey; Jason H. Yang; Marinka Zitnik; Sylvain V. Costes

doi:10.1038/s42256-023-00617-5

Biomonitoring and precision health in deep space supported by artificial intelligence

Ryan T. Scott, Lauren M. Sanders, Erik L. Antonsen, Jaden J.A. Hastings, Seung min Park, Graham Mackintosh, Robert J. Reynolds, Adrienne L. Hoarfrost, Aenor Sawyer, Casey S. Greene, Benjamin S. Glicksberg, Corey A. Theriot, Daniel C. Berrios, Jack Miller, Joel Babdor, Richard Barker, Sergio E. Baranzini, Afshin Beheshti, Stuart Chalk, Guillermo M. Delgado-AparicioMelissa Haendel, Arif A. Hamid, Philip Heller, Daniel Jamieson, Katelyn J. Jarvis, John Kalantari, Kia Khezeli, Svetlana V. Komarova, Matthieu Komorowski, Prachi Kothiyal, Ashish Mahabal, Uri Manor, Hector Garcia Martin, Christopher E. Mason, Mona Matar, George I. Mias, Jerry G. Myers, Charlotte Nelson, Jonathan Oribello, Patricia Parsons-Wingerter, R. K. Prabhu, Amina Ann Qutub, Jon Rask, Amanda Saravia-Butler, Suchi Saria, Nitin Kumar Singh, Michael Snyder, Frank Soboczenski, Karthik Soman, David Van Valen, Kasthuri Venkateswaran, Liz Warren, Liz Worthey, Jason H. Yang, Marinka Zitnik, Sylvain V. Costes

Research output: Contribution to journal › Review article › peer-review

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Abstract

Human exploration of deep space will involve missions of substantial distance and duration. To effectively mitigate health hazards, paradigm shifts in astronaut health systems are necessary to enable Earth-independent healthcare, rather than Earth-reliant. Here we present a summary of decadal recommendations from a workshop organized by NASA on artificial intelligence, machine learning and modelling applications that offer key solutions toward these space health challenges. The workshop recommended various biomonitoring approaches, biomarker science, spacecraft/habitat hardware, intelligent software and streamlined data management tools in need of development and integration to enable humanity to thrive in deep space. Participants recommended that these components culminate in a maximally automated, autonomous and intelligent Precision Space Health system, to monitor, aggregate and assess biomedical statuses.

Original language	English
Pages (from-to)	196-207
Number of pages	12
Journal	Nature Machine Intelligence
Volume	5
Issue number	3
DOIs	https://doi.org/10.1038/s42256-023-00617-5
State	Published - Mar 2023

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Scott, R. T., Sanders, L. M., Antonsen, E. L., Hastings, J. J. A., Park, S. M., Mackintosh, G., Reynolds, R. J., Hoarfrost, A. L., Sawyer, A., Greene, C. S., Glicksberg, B. S., Theriot, C. A., Berrios, D. C., Miller, J., Babdor, J., Barker, R., Baranzini, S. E., Beheshti, A., Chalk, S., ... Costes, S. V. (2023). Biomonitoring and precision health in deep space supported by artificial intelligence. Nature Machine Intelligence, 5(3), 196-207. https://doi.org/10.1038/s42256-023-00617-5

@article{2ef4e0dfacb048baad365c0076bdf26d,

title = "Biomonitoring and precision health in deep space supported by artificial intelligence",

abstract = "Human exploration of deep space will involve missions of substantial distance and duration. To effectively mitigate health hazards, paradigm shifts in astronaut health systems are necessary to enable Earth-independent healthcare, rather than Earth-reliant. Here we present a summary of decadal recommendations from a workshop organized by NASA on artificial intelligence, machine learning and modelling applications that offer key solutions toward these space health challenges. The workshop recommended various biomonitoring approaches, biomarker science, spacecraft/habitat hardware, intelligent software and streamlined data management tools in need of development and integration to enable humanity to thrive in deep space. Participants recommended that these components culminate in a maximally automated, autonomous and intelligent Precision Space Health system, to monitor, aggregate and assess biomedical statuses.",

author = "Scott, {Ryan T.} and Sanders, {Lauren M.} and Antonsen, {Erik L.} and Hastings, {Jaden J.A.} and Park, {Seung min} and Graham Mackintosh and Reynolds, {Robert J.} and Hoarfrost, {Adrienne L.} and Aenor Sawyer and Greene, {Casey S.} and Glicksberg, {Benjamin S.} and Theriot, {Corey A.} and Berrios, {Daniel C.} and Jack Miller and Joel Babdor and Richard Barker and Baranzini, {Sergio E.} and Afshin Beheshti and Stuart Chalk and Delgado-Aparicio, {Guillermo M.} and Melissa Haendel and Hamid, {Arif A.} and Philip Heller and Daniel Jamieson and Jarvis, {Katelyn J.} and John Kalantari and Kia Khezeli and Komarova, {Svetlana V.} and Matthieu Komorowski and Prachi Kothiyal and Ashish Mahabal and Uri Manor and {Garcia Martin}, Hector and Mason, {Christopher E.} and Mona Matar and Mias, {George I.} and Myers, {Jerry G.} and Charlotte Nelson and Jonathan Oribello and Patricia Parsons-Wingerter and Prabhu, {R. K.} and Qutub, {Amina Ann} and Jon Rask and Amanda Saravia-Butler and Suchi Saria and Singh, {Nitin Kumar} and Michael Snyder and Frank Soboczenski and Karthik Soman and {Van Valen}, David and Kasthuri Venkateswaran and Liz Warren and Liz Worthey and Yang, {Jason H.} and Marinka Zitnik and Costes, {Sylvain V.}",

note = "Funding Information: We thank all June 2021 participants and speakers at the {\textquoteleft}NASA Workshop on Artificial Intelligence & Modeling for Space Biology{\textquoteright}. Thanks go to the NASA Space Biology Program, part of the NASA Biological and Physical Sciences Division within the NASA Science Mission Directorate, as well as the NASA Human Research Program (HRP). We also thank the Space Biosciences Division and Space Biology at Ames Research Center (ARC), especially D. Ly, R. Vik and P. Vaishampayan. We are grateful for the support provided by NASA GeneLab and the NASA Ames Life Sciences Data Archive. Additional thanks go to S. Bhattacharya (NASA Space Biology Program Scientist), K. Martin (ARC Lead of Exploration Medical Capability (an Element of HRP)), as well as L. Lewis (ARC NASA HRP Lead). S.V.C. is funded by NASA Human Research Program grant NNJ16HP24I. S.E.B. holds the Heidrich Family and Friends Endowed Chair in Neurology at UCSF. S.E.B. also holds the Distinguished Professorship I in Neurology at UCSF. S.E.B. is funded by an NSF Convergence Accelerator award (2033569) and NIH/NCATS Translator award (1OT2TR003450). G.I.M. was supported by the Translational Research Institute for Space Health, through NASA NNX16AO69A (project no. T0412). E.L.A. was supported by the Translational Research Institute for Space Health, through NASA NNX16AO69A. C.E.M. acknowledges NASA grants NNX14AH50G and NNX17AB26G. This work was also part of the DOE Agile BioFoundry, supported by the US Department of Energy, Energy Efficiency and Renewable Energy, Bioenergy Technologies Office, and the DOE Joint BioEnergy Institute, supported by the Office of Science, Office of Biological and Environmental Research, through contract no. DE-AC02-05CH11231 between Lawrence Berkeley National Laboratory and the US Department of Energy. S.V.K. is funded by the Canadian Space Agency (19HLSRM04) and Natural Sciences and Engineering Research Council (NSERC, RGPIN-288253). J.H.Y. is funded by NIH grant no. R00 GM118907 and the Agilent Early Career Professor Award. Funding Information: We thank all June 2021 participants and speakers at the {\textquoteleft}NASA Workshop on Artificial Intelligence & Modeling for Space Biology{\textquoteright}. Thanks go to the NASA Space Biology Program, part of the NASA Biological and Physical Sciences Division within the NASA Science Mission Directorate, as well as the NASA Human Research Program (HRP). We also thank the Space Biosciences Division and Space Biology at Ames Research Center (ARC), especially D. Ly, R. Vik and P. Vaishampayan. We are grateful for the support provided by NASA GeneLab and the NASA Ames Life Sciences Data Archive. Additional thanks go to S. Bhattacharya (NASA Space Biology Program Scientist), K. Martin (ARC Lead of Exploration Medical Capability (an Element of HRP)), as well as L. Lewis (ARC NASA HRP Lead). S.V.C. is funded by NASA Human Research Program grant NNJ16HP24I. S.E.B. holds the Heidrich Family and Friends Endowed Chair in Neurology at UCSF. S.E.B. also holds the Distinguished Professorship I in Neurology at UCSF. S.E.B. is funded by an NSF Convergence Accelerator award (2033569) and NIH/NCATS Translator award (1OT2TR003450). G.I.M. was supported by the Translational Research Institute for Space Health, through NASA NNX16AO69A (project no. T0412). E.L.A. was supported by the Translational Research Institute for Space Health, through NASA NNX16AO69A. C.E.M. acknowledges NASA grants NNX14AH50G and NNX17AB26G. This work was also part of the DOE Agile BioFoundry, supported by the US Department of Energy, Energy Efficiency and Renewable Energy, Bioenergy Technologies Office, and the DOE Joint BioEnergy Institute, supported by the Office of Science, Office of Biological and Environmental Research, through contract no. DE-AC02-05CH11231 between Lawrence Berkeley National Laboratory and the US Department of Energy. S.V.K. is funded by the Canadian Space Agency (19HLSRM04) and Natural Sciences and Engineering Research Council (NSERC, RGPIN-288253). J.H.Y. is funded by NIH grant no. R00 GM118907 and the Agilent Early Career Professor Award. Publisher Copyright: {\textcopyright} 2023, This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.",

year = "2023",

month = mar,

doi = "10.1038/s42256-023-00617-5",

language = "English",

volume = "5",

pages = "196--207",

journal = "Nature Machine Intelligence",

issn = "2522-5839",

publisher = "Springer Nature Switzerland AG",

number = "3",

}

Scott, RT, Sanders, LM, Antonsen, EL, Hastings, JJA, Park, SM, Mackintosh, G, Reynolds, RJ, Hoarfrost, AL, Sawyer, A, Greene, CS, Glicksberg, BS, Theriot, CA, Berrios, DC, Miller, J, Babdor, J, Barker, R, Baranzini, SE, Beheshti, A, Chalk, S, Delgado-Aparicio, GM, Haendel, M, Hamid, AA, Heller, P, Jamieson, D, Jarvis, KJ, Kalantari, J, Khezeli, K, Komarova, SV, Komorowski, M, Kothiyal, P, Mahabal, A, Manor, U, Garcia Martin, H, Mason, CE, Matar, M, Mias, GI, Myers, JG, Nelson, C, Oribello, J, Parsons-Wingerter, P, Prabhu, RK, Qutub, AA, Rask, J, Saravia-Butler, A, Saria, S, Singh, NK, Snyder, M, Soboczenski, F, Soman, K, Van Valen, D, Venkateswaran, K, Warren, L, Worthey, L, Yang, JH, Zitnik, M & Costes, SV 2023, 'Biomonitoring and precision health in deep space supported by artificial intelligence', Nature Machine Intelligence, vol. 5, no. 3, pp. 196-207. https://doi.org/10.1038/s42256-023-00617-5

TY - JOUR

T1 - Biomonitoring and precision health in deep space supported by artificial intelligence

AU - Scott, Ryan T.

AU - Sanders, Lauren M.

AU - Antonsen, Erik L.

AU - Hastings, Jaden J.A.

AU - Park, Seung min

AU - Mackintosh, Graham

AU - Reynolds, Robert J.

AU - Hoarfrost, Adrienne L.

AU - Sawyer, Aenor

AU - Greene, Casey S.

AU - Glicksberg, Benjamin S.

AU - Theriot, Corey A.

AU - Berrios, Daniel C.

AU - Miller, Jack

AU - Babdor, Joel

AU - Barker, Richard

AU - Baranzini, Sergio E.

AU - Beheshti, Afshin

AU - Chalk, Stuart

AU - Delgado-Aparicio, Guillermo M.

AU - Haendel, Melissa

AU - Hamid, Arif A.

AU - Heller, Philip

AU - Jamieson, Daniel

AU - Jarvis, Katelyn J.

AU - Kalantari, John

AU - Khezeli, Kia

AU - Komarova, Svetlana V.

AU - Komorowski, Matthieu

AU - Kothiyal, Prachi

AU - Mahabal, Ashish

AU - Manor, Uri

AU - Garcia Martin, Hector

AU - Mason, Christopher E.

AU - Matar, Mona

AU - Mias, George I.

AU - Myers, Jerry G.

AU - Nelson, Charlotte

AU - Oribello, Jonathan

AU - Parsons-Wingerter, Patricia

AU - Prabhu, R. K.

AU - Qutub, Amina Ann

AU - Rask, Jon

AU - Saravia-Butler, Amanda

AU - Saria, Suchi

AU - Singh, Nitin Kumar

AU - Snyder, Michael

AU - Soboczenski, Frank

AU - Soman, Karthik

AU - Van Valen, David

AU - Venkateswaran, Kasthuri

AU - Warren, Liz

AU - Worthey, Liz

AU - Yang, Jason H.

AU - Zitnik, Marinka

AU - Costes, Sylvain V.

N1 - Funding Information: We thank all June 2021 participants and speakers at the ‘NASA Workshop on Artificial Intelligence & Modeling for Space Biology’. Thanks go to the NASA Space Biology Program, part of the NASA Biological and Physical Sciences Division within the NASA Science Mission Directorate, as well as the NASA Human Research Program (HRP). We also thank the Space Biosciences Division and Space Biology at Ames Research Center (ARC), especially D. Ly, R. Vik and P. Vaishampayan. We are grateful for the support provided by NASA GeneLab and the NASA Ames Life Sciences Data Archive. Additional thanks go to S. Bhattacharya (NASA Space Biology Program Scientist), K. Martin (ARC Lead of Exploration Medical Capability (an Element of HRP)), as well as L. Lewis (ARC NASA HRP Lead). S.V.C. is funded by NASA Human Research Program grant NNJ16HP24I. S.E.B. holds the Heidrich Family and Friends Endowed Chair in Neurology at UCSF. S.E.B. also holds the Distinguished Professorship I in Neurology at UCSF. S.E.B. is funded by an NSF Convergence Accelerator award (2033569) and NIH/NCATS Translator award (1OT2TR003450). G.I.M. was supported by the Translational Research Institute for Space Health, through NASA NNX16AO69A (project no. T0412). E.L.A. was supported by the Translational Research Institute for Space Health, through NASA NNX16AO69A. C.E.M. acknowledges NASA grants NNX14AH50G and NNX17AB26G. This work was also part of the DOE Agile BioFoundry, supported by the US Department of Energy, Energy Efficiency and Renewable Energy, Bioenergy Technologies Office, and the DOE Joint BioEnergy Institute, supported by the Office of Science, Office of Biological and Environmental Research, through contract no. DE-AC02-05CH11231 between Lawrence Berkeley National Laboratory and the US Department of Energy. S.V.K. is funded by the Canadian Space Agency (19HLSRM04) and Natural Sciences and Engineering Research Council (NSERC, RGPIN-288253). J.H.Y. is funded by NIH grant no. R00 GM118907 and the Agilent Early Career Professor Award. Funding Information: We thank all June 2021 participants and speakers at the ‘NASA Workshop on Artificial Intelligence & Modeling for Space Biology’. Thanks go to the NASA Space Biology Program, part of the NASA Biological and Physical Sciences Division within the NASA Science Mission Directorate, as well as the NASA Human Research Program (HRP). We also thank the Space Biosciences Division and Space Biology at Ames Research Center (ARC), especially D. Ly, R. Vik and P. Vaishampayan. We are grateful for the support provided by NASA GeneLab and the NASA Ames Life Sciences Data Archive. Additional thanks go to S. Bhattacharya (NASA Space Biology Program Scientist), K. Martin (ARC Lead of Exploration Medical Capability (an Element of HRP)), as well as L. Lewis (ARC NASA HRP Lead). S.V.C. is funded by NASA Human Research Program grant NNJ16HP24I. S.E.B. holds the Heidrich Family and Friends Endowed Chair in Neurology at UCSF. S.E.B. also holds the Distinguished Professorship I in Neurology at UCSF. S.E.B. is funded by an NSF Convergence Accelerator award (2033569) and NIH/NCATS Translator award (1OT2TR003450). G.I.M. was supported by the Translational Research Institute for Space Health, through NASA NNX16AO69A (project no. T0412). E.L.A. was supported by the Translational Research Institute for Space Health, through NASA NNX16AO69A. C.E.M. acknowledges NASA grants NNX14AH50G and NNX17AB26G. This work was also part of the DOE Agile BioFoundry, supported by the US Department of Energy, Energy Efficiency and Renewable Energy, Bioenergy Technologies Office, and the DOE Joint BioEnergy Institute, supported by the Office of Science, Office of Biological and Environmental Research, through contract no. DE-AC02-05CH11231 between Lawrence Berkeley National Laboratory and the US Department of Energy. S.V.K. is funded by the Canadian Space Agency (19HLSRM04) and Natural Sciences and Engineering Research Council (NSERC, RGPIN-288253). J.H.Y. is funded by NIH grant no. R00 GM118907 and the Agilent Early Career Professor Award. Publisher Copyright: © 2023, This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.

PY - 2023/3

Y1 - 2023/3

N2 - Human exploration of deep space will involve missions of substantial distance and duration. To effectively mitigate health hazards, paradigm shifts in astronaut health systems are necessary to enable Earth-independent healthcare, rather than Earth-reliant. Here we present a summary of decadal recommendations from a workshop organized by NASA on artificial intelligence, machine learning and modelling applications that offer key solutions toward these space health challenges. The workshop recommended various biomonitoring approaches, biomarker science, spacecraft/habitat hardware, intelligent software and streamlined data management tools in need of development and integration to enable humanity to thrive in deep space. Participants recommended that these components culminate in a maximally automated, autonomous and intelligent Precision Space Health system, to monitor, aggregate and assess biomedical statuses.

AB - Human exploration of deep space will involve missions of substantial distance and duration. To effectively mitigate health hazards, paradigm shifts in astronaut health systems are necessary to enable Earth-independent healthcare, rather than Earth-reliant. Here we present a summary of decadal recommendations from a workshop organized by NASA on artificial intelligence, machine learning and modelling applications that offer key solutions toward these space health challenges. The workshop recommended various biomonitoring approaches, biomarker science, spacecraft/habitat hardware, intelligent software and streamlined data management tools in need of development and integration to enable humanity to thrive in deep space. Participants recommended that these components culminate in a maximally automated, autonomous and intelligent Precision Space Health system, to monitor, aggregate and assess biomedical statuses.

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U2 - 10.1038/s42256-023-00617-5

DO - 10.1038/s42256-023-00617-5

M3 - Review article

AN - SCOPUS:85150945285

SN - 2522-5839

VL - 5

SP - 196

EP - 207

JO - Nature Machine Intelligence

JF - Nature Machine Intelligence

IS - 3

ER -

Biomonitoring and precision health in deep space supported by artificial intelligence

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