Harmonization of Physiological Data in Neurocritical Care: Challenges and a Path Forward

Abstract Continuous multimodal monitoring in neurocritical care provides valuable insights into the dynamics of the injured brain. Unfortunately, the “readiness” of this data for robust artificial intelligence (AI) and machine learning (ML) applications is low and presents a significant barrier for...
Ausführliche Beschreibung

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Autor*in:	Moberg, Richard [verfasserIn] Moyer, Ethan Jacob Olson, DaiWai Rosenthal, Eric Foreman, Brandon

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Big data Critical care Data annotation Informatics Physiologic monitoring Standards

Anmerkung:	© Springer Science+Business Media, LLC, part of Springer Nature and Neurocritical Care Society 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Neurocritical care - New York, NY : Springer, 2004, 37(2022), Suppl 2 vom: 01. Juni, Seite 202-205
Übergeordnetes Werk:	volume:37 ; year:2022 ; number:Suppl 2 ; day:01 ; month:06 ; pages:202-205

Links:	Volltext

DOI / URN:	10.1007/s12028-022-01524-0

Katalog-ID:	SPR047740647

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allfields	10.1007/s12028-022-01524-0 doi (DE-627)SPR047740647 (SPR)s12028-022-01524-0-e DE-627 ger DE-627 rakwb eng Moberg, Richard verfasserin (orcid)0000-0002-7950-8629 aut Harmonization of Physiological Data in Neurocritical Care: Challenges and a Path Forward 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature and Neurocritical Care Society 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Continuous multimodal monitoring in neurocritical care provides valuable insights into the dynamics of the injured brain. Unfortunately, the “readiness” of this data for robust artificial intelligence (AI) and machine learning (ML) applications is low and presents a significant barrier for advancement. Harmonization standards and tools to implement those standards are key to overcoming existing barriers. Consensus in our professional community is essential for success. Big data (dpeaa)DE-He213 Critical care (dpeaa)DE-He213 Data annotation (dpeaa)DE-He213 Informatics (dpeaa)DE-He213 Physiologic monitoring (dpeaa)DE-He213 Standards (dpeaa)DE-He213 Moyer, Ethan Jacob aut Olson, DaiWai aut Rosenthal, Eric aut Foreman, Brandon aut Enthalten in Neurocritical care New York, NY : Springer, 2004 37(2022), Suppl 2 vom: 01. Juni, Seite 202-205 (DE-627)478509855 (DE-600)2176033-0 1556-0961 nnns volume:37 year:2022 number:Suppl 2 day:01 month:06 pages:202-205 https://dx.doi.org/10.1007/s12028-022-01524-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 37 2022 Suppl 2 01 06 202-205
spelling	10.1007/s12028-022-01524-0 doi (DE-627)SPR047740647 (SPR)s12028-022-01524-0-e DE-627 ger DE-627 rakwb eng Moberg, Richard verfasserin (orcid)0000-0002-7950-8629 aut Harmonization of Physiological Data in Neurocritical Care: Challenges and a Path Forward 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature and Neurocritical Care Society 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Continuous multimodal monitoring in neurocritical care provides valuable insights into the dynamics of the injured brain. Unfortunately, the “readiness” of this data for robust artificial intelligence (AI) and machine learning (ML) applications is low and presents a significant barrier for advancement. Harmonization standards and tools to implement those standards are key to overcoming existing barriers. Consensus in our professional community is essential for success. Big data (dpeaa)DE-He213 Critical care (dpeaa)DE-He213 Data annotation (dpeaa)DE-He213 Informatics (dpeaa)DE-He213 Physiologic monitoring (dpeaa)DE-He213 Standards (dpeaa)DE-He213 Moyer, Ethan Jacob aut Olson, DaiWai aut Rosenthal, Eric aut Foreman, Brandon aut Enthalten in Neurocritical care New York, NY : Springer, 2004 37(2022), Suppl 2 vom: 01. Juni, Seite 202-205 (DE-627)478509855 (DE-600)2176033-0 1556-0961 nnns volume:37 year:2022 number:Suppl 2 day:01 month:06 pages:202-205 https://dx.doi.org/10.1007/s12028-022-01524-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 37 2022 Suppl 2 01 06 202-205
allfields_unstemmed	10.1007/s12028-022-01524-0 doi (DE-627)SPR047740647 (SPR)s12028-022-01524-0-e DE-627 ger DE-627 rakwb eng Moberg, Richard verfasserin (orcid)0000-0002-7950-8629 aut Harmonization of Physiological Data in Neurocritical Care: Challenges and a Path Forward 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature and Neurocritical Care Society 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Continuous multimodal monitoring in neurocritical care provides valuable insights into the dynamics of the injured brain. Unfortunately, the “readiness” of this data for robust artificial intelligence (AI) and machine learning (ML) applications is low and presents a significant barrier for advancement. Harmonization standards and tools to implement those standards are key to overcoming existing barriers. Consensus in our professional community is essential for success. Big data (dpeaa)DE-He213 Critical care (dpeaa)DE-He213 Data annotation (dpeaa)DE-He213 Informatics (dpeaa)DE-He213 Physiologic monitoring (dpeaa)DE-He213 Standards (dpeaa)DE-He213 Moyer, Ethan Jacob aut Olson, DaiWai aut Rosenthal, Eric aut Foreman, Brandon aut Enthalten in Neurocritical care New York, NY : Springer, 2004 37(2022), Suppl 2 vom: 01. Juni, Seite 202-205 (DE-627)478509855 (DE-600)2176033-0 1556-0961 nnns volume:37 year:2022 number:Suppl 2 day:01 month:06 pages:202-205 https://dx.doi.org/10.1007/s12028-022-01524-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 37 2022 Suppl 2 01 06 202-205
allfieldsGer	10.1007/s12028-022-01524-0 doi (DE-627)SPR047740647 (SPR)s12028-022-01524-0-e DE-627 ger DE-627 rakwb eng Moberg, Richard verfasserin (orcid)0000-0002-7950-8629 aut Harmonization of Physiological Data in Neurocritical Care: Challenges and a Path Forward 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature and Neurocritical Care Society 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Continuous multimodal monitoring in neurocritical care provides valuable insights into the dynamics of the injured brain. Unfortunately, the “readiness” of this data for robust artificial intelligence (AI) and machine learning (ML) applications is low and presents a significant barrier for advancement. Harmonization standards and tools to implement those standards are key to overcoming existing barriers. Consensus in our professional community is essential for success. Big data (dpeaa)DE-He213 Critical care (dpeaa)DE-He213 Data annotation (dpeaa)DE-He213 Informatics (dpeaa)DE-He213 Physiologic monitoring (dpeaa)DE-He213 Standards (dpeaa)DE-He213 Moyer, Ethan Jacob aut Olson, DaiWai aut Rosenthal, Eric aut Foreman, Brandon aut Enthalten in Neurocritical care New York, NY : Springer, 2004 37(2022), Suppl 2 vom: 01. Juni, Seite 202-205 (DE-627)478509855 (DE-600)2176033-0 1556-0961 nnns volume:37 year:2022 number:Suppl 2 day:01 month:06 pages:202-205 https://dx.doi.org/10.1007/s12028-022-01524-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 37 2022 Suppl 2 01 06 202-205
allfieldsSound	10.1007/s12028-022-01524-0 doi (DE-627)SPR047740647 (SPR)s12028-022-01524-0-e DE-627 ger DE-627 rakwb eng Moberg, Richard verfasserin (orcid)0000-0002-7950-8629 aut Harmonization of Physiological Data in Neurocritical Care: Challenges and a Path Forward 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature and Neurocritical Care Society 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Continuous multimodal monitoring in neurocritical care provides valuable insights into the dynamics of the injured brain. Unfortunately, the “readiness” of this data for robust artificial intelligence (AI) and machine learning (ML) applications is low and presents a significant barrier for advancement. Harmonization standards and tools to implement those standards are key to overcoming existing barriers. Consensus in our professional community is essential for success. Big data (dpeaa)DE-He213 Critical care (dpeaa)DE-He213 Data annotation (dpeaa)DE-He213 Informatics (dpeaa)DE-He213 Physiologic monitoring (dpeaa)DE-He213 Standards (dpeaa)DE-He213 Moyer, Ethan Jacob aut Olson, DaiWai aut Rosenthal, Eric aut Foreman, Brandon aut Enthalten in Neurocritical care New York, NY : Springer, 2004 37(2022), Suppl 2 vom: 01. Juni, Seite 202-205 (DE-627)478509855 (DE-600)2176033-0 1556-0961 nnns volume:37 year:2022 number:Suppl 2 day:01 month:06 pages:202-205 https://dx.doi.org/10.1007/s12028-022-01524-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 37 2022 Suppl 2 01 06 202-205
language	English
source	Enthalten in Neurocritical care 37(2022), Suppl 2 vom: 01. Juni, Seite 202-205 volume:37 year:2022 number:Suppl 2 day:01 month:06 pages:202-205
sourceStr	Enthalten in Neurocritical care 37(2022), Suppl 2 vom: 01. Juni, Seite 202-205 volume:37 year:2022 number:Suppl 2 day:01 month:06 pages:202-205
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Big data Critical care Data annotation Informatics Physiologic monitoring Standards
isfreeaccess_bool	false
container_title	Neurocritical care
authorswithroles_txt_mv	Moberg, Richard @@aut@@ Moyer, Ethan Jacob @@aut@@ Olson, DaiWai @@aut@@ Rosenthal, Eric @@aut@@ Foreman, Brandon @@aut@@
publishDateDaySort_date	2022-06-01T00:00:00Z
hierarchy_top_id	478509855
id	SPR047740647
language_de	englisch
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title_sort	harmonization of physiological data in neurocritical care: challenges and a path forward
title_auth	Harmonization of Physiological Data in Neurocritical Care: Challenges and a Path Forward
abstract	Abstract Continuous multimodal monitoring in neurocritical care provides valuable insights into the dynamics of the injured brain. Unfortunately, the “readiness” of this data for robust artificial intelligence (AI) and machine learning (ML) applications is low and presents a significant barrier for advancement. Harmonization standards and tools to implement those standards are key to overcoming existing barriers. Consensus in our professional community is essential for success. © Springer Science+Business Media, LLC, part of Springer Nature and Neurocritical Care Society 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract Continuous multimodal monitoring in neurocritical care provides valuable insights into the dynamics of the injured brain. Unfortunately, the “readiness” of this data for robust artificial intelligence (AI) and machine learning (ML) applications is low and presents a significant barrier for advancement. Harmonization standards and tools to implement those standards are key to overcoming existing barriers. Consensus in our professional community is essential for success. © Springer Science+Business Media, LLC, part of Springer Nature and Neurocritical Care Society 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract Continuous multimodal monitoring in neurocritical care provides valuable insights into the dynamics of the injured brain. Unfortunately, the “readiness” of this data for robust artificial intelligence (AI) and machine learning (ML) applications is low and presents a significant barrier for advancement. Harmonization standards and tools to implement those standards are key to overcoming existing barriers. Consensus in our professional community is essential for success. © Springer Science+Business Media, LLC, part of Springer Nature and Neurocritical Care Society 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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container_issue	Suppl 2
title_short	Harmonization of Physiological Data in Neurocritical Care: Challenges and a Path Forward
url	https://dx.doi.org/10.1007/s12028-022-01524-0
remote_bool	true
author2	Moyer, Ethan Jacob Olson, DaiWai Rosenthal, Eric Foreman, Brandon
author2Str	Moyer, Ethan Jacob Olson, DaiWai Rosenthal, Eric Foreman, Brandon
ppnlink	478509855
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doi_str	10.1007/s12028-022-01524-0
up_date	2024-07-03T14:41:01.899Z
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