A deep learning approach for predicting severity of COVID-19 patients using a parsimonious set of laboratory markers

Summary: The SARS-CoV-2 virus has caused tremendous healthcare burden worldwide. Our focus was to develop a practical and easy-to-deploy system to predict the severe manifestation of disease in patients with COVID-19 with an aim to assist clinicians in triage and treatment decisions. Our proposed pr...
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Autor*in:	Vivek Singh [verfasserIn] Rishikesan Kamaleswaran [verfasserIn] Donald Chalfin [verfasserIn] Antonio Buño-Soto [verfasserIn] Janika San Roman [verfasserIn] Edith Rojas-Kenney [verfasserIn] Ross Molinaro [verfasserIn] Sabine von Sengbusch [verfasserIn] Parsa Hodjat [verfasserIn] Dorin Comaniciu [verfasserIn] Ali Kamen [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Classification Description: Virology Diagnostics Artificial intelligence Machine learning

Übergeordnetes Werk:	In: iScience - Elsevier, 2019, 24(2021), 12, Seite 103523-
Übergeordnetes Werk:	volume:24 ; year:2021 ; number:12 ; pages:103523-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.isci.2021.103523

Katalog-ID:	DOAJ019144288

Internformat


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allfields	10.1016/j.isci.2021.103523 doi (DE-627)DOAJ019144288 (DE-599)DOAJ554dc0918c694e56b7f8142b1b9880af DE-627 ger DE-627 rakwb eng Vivek Singh verfasserin aut A deep learning approach for predicting severity of COVID-19 patients using a parsimonious set of laboratory markers 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Summary: The SARS-CoV-2 virus has caused tremendous healthcare burden worldwide. Our focus was to develop a practical and easy-to-deploy system to predict the severe manifestation of disease in patients with COVID-19 with an aim to assist clinicians in triage and treatment decisions. Our proposed predictive algorithm is a trained artificial intelligence-based network using 8,427 COVID-19 patient records from four healthcare systems. The model provides a severity risk score along with likelihoods of various clinical outcomes, namely ventilator use and mortality. The trained model using patient age and nine laboratory markers has the prediction accuracy with an area under the curve (AUC) of 0.78, 95% CI: 0.77–0.82, and the negative predictive value NPV of 0.86, 95% CI: 0.84–0.88 for the need to use a ventilator and has an accuracy with AUC of 0.85, 95% CI: 0.84–0.86, and the NPV of 0.94, 95% CI: 0.92–0.96 for predicting in-hospital 30-day mortality. Classification Description: Virology Diagnostics Artificial intelligence Machine learning Science Q Rishikesan Kamaleswaran verfasserin aut Donald Chalfin verfasserin aut Antonio Buño-Soto verfasserin aut Janika San Roman verfasserin aut Edith Rojas-Kenney verfasserin aut Ross Molinaro verfasserin aut Sabine von Sengbusch verfasserin aut Parsa Hodjat verfasserin aut Dorin Comaniciu verfasserin aut Ali Kamen verfasserin aut In iScience Elsevier, 2019 24(2021), 12, Seite 103523- (DE-627)1019532106 25890042 nnns volume:24 year:2021 number:12 pages:103523- https://doi.org/10.1016/j.isci.2021.103523 kostenfrei https://doaj.org/article/554dc0918c694e56b7f8142b1b9880af kostenfrei http://www.sciencedirect.com/science/article/pii/S2589004221014942 kostenfrei https://doaj.org/toc/2589-0042 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 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_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4333 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 24 2021 12 103523-
spelling	10.1016/j.isci.2021.103523 doi (DE-627)DOAJ019144288 (DE-599)DOAJ554dc0918c694e56b7f8142b1b9880af DE-627 ger DE-627 rakwb eng Vivek Singh verfasserin aut A deep learning approach for predicting severity of COVID-19 patients using a parsimonious set of laboratory markers 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Summary: The SARS-CoV-2 virus has caused tremendous healthcare burden worldwide. Our focus was to develop a practical and easy-to-deploy system to predict the severe manifestation of disease in patients with COVID-19 with an aim to assist clinicians in triage and treatment decisions. Our proposed predictive algorithm is a trained artificial intelligence-based network using 8,427 COVID-19 patient records from four healthcare systems. The model provides a severity risk score along with likelihoods of various clinical outcomes, namely ventilator use and mortality. The trained model using patient age and nine laboratory markers has the prediction accuracy with an area under the curve (AUC) of 0.78, 95% CI: 0.77–0.82, and the negative predictive value NPV of 0.86, 95% CI: 0.84–0.88 for the need to use a ventilator and has an accuracy with AUC of 0.85, 95% CI: 0.84–0.86, and the NPV of 0.94, 95% CI: 0.92–0.96 for predicting in-hospital 30-day mortality. Classification Description: Virology Diagnostics Artificial intelligence Machine learning Science Q Rishikesan Kamaleswaran verfasserin aut Donald Chalfin verfasserin aut Antonio Buño-Soto verfasserin aut Janika San Roman verfasserin aut Edith Rojas-Kenney verfasserin aut Ross Molinaro verfasserin aut Sabine von Sengbusch verfasserin aut Parsa Hodjat verfasserin aut Dorin Comaniciu verfasserin aut Ali Kamen verfasserin aut In iScience Elsevier, 2019 24(2021), 12, Seite 103523- (DE-627)1019532106 25890042 nnns volume:24 year:2021 number:12 pages:103523- https://doi.org/10.1016/j.isci.2021.103523 kostenfrei https://doaj.org/article/554dc0918c694e56b7f8142b1b9880af kostenfrei http://www.sciencedirect.com/science/article/pii/S2589004221014942 kostenfrei https://doaj.org/toc/2589-0042 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 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_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4333 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 24 2021 12 103523-
allfields_unstemmed	10.1016/j.isci.2021.103523 doi (DE-627)DOAJ019144288 (DE-599)DOAJ554dc0918c694e56b7f8142b1b9880af DE-627 ger DE-627 rakwb eng Vivek Singh verfasserin aut A deep learning approach for predicting severity of COVID-19 patients using a parsimonious set of laboratory markers 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Summary: The SARS-CoV-2 virus has caused tremendous healthcare burden worldwide. Our focus was to develop a practical and easy-to-deploy system to predict the severe manifestation of disease in patients with COVID-19 with an aim to assist clinicians in triage and treatment decisions. Our proposed predictive algorithm is a trained artificial intelligence-based network using 8,427 COVID-19 patient records from four healthcare systems. The model provides a severity risk score along with likelihoods of various clinical outcomes, namely ventilator use and mortality. The trained model using patient age and nine laboratory markers has the prediction accuracy with an area under the curve (AUC) of 0.78, 95% CI: 0.77–0.82, and the negative predictive value NPV of 0.86, 95% CI: 0.84–0.88 for the need to use a ventilator and has an accuracy with AUC of 0.85, 95% CI: 0.84–0.86, and the NPV of 0.94, 95% CI: 0.92–0.96 for predicting in-hospital 30-day mortality. Classification Description: Virology Diagnostics Artificial intelligence Machine learning Science Q Rishikesan Kamaleswaran verfasserin aut Donald Chalfin verfasserin aut Antonio Buño-Soto verfasserin aut Janika San Roman verfasserin aut Edith Rojas-Kenney verfasserin aut Ross Molinaro verfasserin aut Sabine von Sengbusch verfasserin aut Parsa Hodjat verfasserin aut Dorin Comaniciu verfasserin aut Ali Kamen verfasserin aut In iScience Elsevier, 2019 24(2021), 12, Seite 103523- (DE-627)1019532106 25890042 nnns volume:24 year:2021 number:12 pages:103523- https://doi.org/10.1016/j.isci.2021.103523 kostenfrei https://doaj.org/article/554dc0918c694e56b7f8142b1b9880af kostenfrei http://www.sciencedirect.com/science/article/pii/S2589004221014942 kostenfrei https://doaj.org/toc/2589-0042 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 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_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4333 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 24 2021 12 103523-
allfieldsGer	10.1016/j.isci.2021.103523 doi (DE-627)DOAJ019144288 (DE-599)DOAJ554dc0918c694e56b7f8142b1b9880af DE-627 ger DE-627 rakwb eng Vivek Singh verfasserin aut A deep learning approach for predicting severity of COVID-19 patients using a parsimonious set of laboratory markers 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Summary: The SARS-CoV-2 virus has caused tremendous healthcare burden worldwide. Our focus was to develop a practical and easy-to-deploy system to predict the severe manifestation of disease in patients with COVID-19 with an aim to assist clinicians in triage and treatment decisions. Our proposed predictive algorithm is a trained artificial intelligence-based network using 8,427 COVID-19 patient records from four healthcare systems. The model provides a severity risk score along with likelihoods of various clinical outcomes, namely ventilator use and mortality. The trained model using patient age and nine laboratory markers has the prediction accuracy with an area under the curve (AUC) of 0.78, 95% CI: 0.77–0.82, and the negative predictive value NPV of 0.86, 95% CI: 0.84–0.88 for the need to use a ventilator and has an accuracy with AUC of 0.85, 95% CI: 0.84–0.86, and the NPV of 0.94, 95% CI: 0.92–0.96 for predicting in-hospital 30-day mortality. Classification Description: Virology Diagnostics Artificial intelligence Machine learning Science Q Rishikesan Kamaleswaran verfasserin aut Donald Chalfin verfasserin aut Antonio Buño-Soto verfasserin aut Janika San Roman verfasserin aut Edith Rojas-Kenney verfasserin aut Ross Molinaro verfasserin aut Sabine von Sengbusch verfasserin aut Parsa Hodjat verfasserin aut Dorin Comaniciu verfasserin aut Ali Kamen verfasserin aut In iScience Elsevier, 2019 24(2021), 12, Seite 103523- (DE-627)1019532106 25890042 nnns volume:24 year:2021 number:12 pages:103523- https://doi.org/10.1016/j.isci.2021.103523 kostenfrei https://doaj.org/article/554dc0918c694e56b7f8142b1b9880af kostenfrei http://www.sciencedirect.com/science/article/pii/S2589004221014942 kostenfrei https://doaj.org/toc/2589-0042 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 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_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4333 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 24 2021 12 103523-
allfieldsSound	10.1016/j.isci.2021.103523 doi (DE-627)DOAJ019144288 (DE-599)DOAJ554dc0918c694e56b7f8142b1b9880af DE-627 ger DE-627 rakwb eng Vivek Singh verfasserin aut A deep learning approach for predicting severity of COVID-19 patients using a parsimonious set of laboratory markers 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Summary: The SARS-CoV-2 virus has caused tremendous healthcare burden worldwide. Our focus was to develop a practical and easy-to-deploy system to predict the severe manifestation of disease in patients with COVID-19 with an aim to assist clinicians in triage and treatment decisions. Our proposed predictive algorithm is a trained artificial intelligence-based network using 8,427 COVID-19 patient records from four healthcare systems. The model provides a severity risk score along with likelihoods of various clinical outcomes, namely ventilator use and mortality. The trained model using patient age and nine laboratory markers has the prediction accuracy with an area under the curve (AUC) of 0.78, 95% CI: 0.77–0.82, and the negative predictive value NPV of 0.86, 95% CI: 0.84–0.88 for the need to use a ventilator and has an accuracy with AUC of 0.85, 95% CI: 0.84–0.86, and the NPV of 0.94, 95% CI: 0.92–0.96 for predicting in-hospital 30-day mortality. Classification Description: Virology Diagnostics Artificial intelligence Machine learning Science Q Rishikesan Kamaleswaran verfasserin aut Donald Chalfin verfasserin aut Antonio Buño-Soto verfasserin aut Janika San Roman verfasserin aut Edith Rojas-Kenney verfasserin aut Ross Molinaro verfasserin aut Sabine von Sengbusch verfasserin aut Parsa Hodjat verfasserin aut Dorin Comaniciu verfasserin aut Ali Kamen verfasserin aut In iScience Elsevier, 2019 24(2021), 12, Seite 103523- (DE-627)1019532106 25890042 nnns volume:24 year:2021 number:12 pages:103523- https://doi.org/10.1016/j.isci.2021.103523 kostenfrei https://doaj.org/article/554dc0918c694e56b7f8142b1b9880af kostenfrei http://www.sciencedirect.com/science/article/pii/S2589004221014942 kostenfrei https://doaj.org/toc/2589-0042 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 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_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4333 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 24 2021 12 103523-
language	English
source	In iScience 24(2021), 12, Seite 103523- volume:24 year:2021 number:12 pages:103523-
sourceStr	In iScience 24(2021), 12, Seite 103523- volume:24 year:2021 number:12 pages:103523-
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Classification Description: Virology Diagnostics Artificial intelligence Machine learning Science Q
isfreeaccess_bool	true
container_title	iScience
authorswithroles_txt_mv	Vivek Singh @@aut@@ Rishikesan Kamaleswaran @@aut@@ Donald Chalfin @@aut@@ Antonio Buño-Soto @@aut@@ Janika San Roman @@aut@@ Edith Rojas-Kenney @@aut@@ Ross Molinaro @@aut@@ Sabine von Sengbusch @@aut@@ Parsa Hodjat @@aut@@ Dorin Comaniciu @@aut@@ Ali Kamen @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	1019532106
id	DOAJ019144288
language_de	englisch
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author	Vivek Singh
spellingShingle	Vivek Singh misc Classification Description: Virology misc Diagnostics misc Artificial intelligence misc Machine learning misc Science misc Q A deep learning approach for predicting severity of COVID-19 patients using a parsimonious set of laboratory markers
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topic_title	A deep learning approach for predicting severity of COVID-19 patients using a parsimonious set of laboratory markers Classification Description: Virology Diagnostics Artificial intelligence Machine learning
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title	A deep learning approach for predicting severity of COVID-19 patients using a parsimonious set of laboratory markers
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title_full	A deep learning approach for predicting severity of COVID-19 patients using a parsimonious set of laboratory markers
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author_browse	Vivek Singh Rishikesan Kamaleswaran Donald Chalfin Antonio Buño-Soto Janika San Roman Edith Rojas-Kenney Ross Molinaro Sabine von Sengbusch Parsa Hodjat Dorin Comaniciu Ali Kamen
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title_sort	deep learning approach for predicting severity of covid-19 patients using a parsimonious set of laboratory markers
title_auth	A deep learning approach for predicting severity of COVID-19 patients using a parsimonious set of laboratory markers
abstract	Summary: The SARS-CoV-2 virus has caused tremendous healthcare burden worldwide. Our focus was to develop a practical and easy-to-deploy system to predict the severe manifestation of disease in patients with COVID-19 with an aim to assist clinicians in triage and treatment decisions. Our proposed predictive algorithm is a trained artificial intelligence-based network using 8,427 COVID-19 patient records from four healthcare systems. The model provides a severity risk score along with likelihoods of various clinical outcomes, namely ventilator use and mortality. The trained model using patient age and nine laboratory markers has the prediction accuracy with an area under the curve (AUC) of 0.78, 95% CI: 0.77–0.82, and the negative predictive value NPV of 0.86, 95% CI: 0.84–0.88 for the need to use a ventilator and has an accuracy with AUC of 0.85, 95% CI: 0.84–0.86, and the NPV of 0.94, 95% CI: 0.92–0.96 for predicting in-hospital 30-day mortality.
abstractGer	Summary: The SARS-CoV-2 virus has caused tremendous healthcare burden worldwide. Our focus was to develop a practical and easy-to-deploy system to predict the severe manifestation of disease in patients with COVID-19 with an aim to assist clinicians in triage and treatment decisions. Our proposed predictive algorithm is a trained artificial intelligence-based network using 8,427 COVID-19 patient records from four healthcare systems. The model provides a severity risk score along with likelihoods of various clinical outcomes, namely ventilator use and mortality. The trained model using patient age and nine laboratory markers has the prediction accuracy with an area under the curve (AUC) of 0.78, 95% CI: 0.77–0.82, and the negative predictive value NPV of 0.86, 95% CI: 0.84–0.88 for the need to use a ventilator and has an accuracy with AUC of 0.85, 95% CI: 0.84–0.86, and the NPV of 0.94, 95% CI: 0.92–0.96 for predicting in-hospital 30-day mortality.
abstract_unstemmed	Summary: The SARS-CoV-2 virus has caused tremendous healthcare burden worldwide. Our focus was to develop a practical and easy-to-deploy system to predict the severe manifestation of disease in patients with COVID-19 with an aim to assist clinicians in triage and treatment decisions. Our proposed predictive algorithm is a trained artificial intelligence-based network using 8,427 COVID-19 patient records from four healthcare systems. The model provides a severity risk score along with likelihoods of various clinical outcomes, namely ventilator use and mortality. The trained model using patient age and nine laboratory markers has the prediction accuracy with an area under the curve (AUC) of 0.78, 95% CI: 0.77–0.82, and the negative predictive value NPV of 0.86, 95% CI: 0.84–0.88 for the need to use a ventilator and has an accuracy with AUC of 0.85, 95% CI: 0.84–0.86, and the NPV of 0.94, 95% CI: 0.92–0.96 for predicting in-hospital 30-day mortality.
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title_short	A deep learning approach for predicting severity of COVID-19 patients using a parsimonious set of laboratory markers
url	https://doi.org/10.1016/j.isci.2021.103523 https://doaj.org/article/554dc0918c694e56b7f8142b1b9880af http://www.sciencedirect.com/science/article/pii/S2589004221014942 https://doaj.org/toc/2589-0042
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up_date	2024-07-03T22:02:25.010Z
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A deep learning approach for predicting severity of COVID-19 patients using a parsimonious set of laboratory markers

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