Predicting Neurological Recovery from Coma After Cardiac Arrest: The George B. Moody PhysioNet Challenge 2023

Matthew A. Reyna; Edilberto Amorim; Reza Sameni; James Weigle; Andoni Elola; Ali Bahrami Rad; Salman Seyedi; Hyeokhyen Kwon; Wei Long Zheng; Mohammad M. Ghassemi; Michel J.A.M. Van Putten; Jeannette Hofmeijer; Nicolas Gaspard; Adithya Sivaraju; Susan T. Herman; Jong Woo Lee; M. Brandon Westover; Gari D. Clifford

Predicting Neurological Recovery from Coma After Cardiac Arrest: The George B. Moody PhysioNet Challenge 2023

Matthew A. Reyna^*, Edilberto Amorim, Reza Sameni, James Weigle, Andoni Elola, Ali Bahrami Rad, Salman Seyedi, Hyeokhyen Kwon, Wei Long Zheng, Mohammad M. Ghassemi, Michel J.A.M. Van Putten, Jeannette Hofmeijer, Nicolas Gaspard, Adithya Sivaraju, Susan T. Herman, Jong Woo Lee, M. Brandon Westover, Gari D. Clifford

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

38 Citations (Scopus)

Abstract

The George B. Moody PhysioNet Challenge 2023 invited teams to develop algorithmic approaches for predicting the recovery of comatose patients after cardiac arrest. A patient's prognosis after the return of spontaneous circulation informs treatment, including the continuation or withdrawal of life support. Brain monitoring with an electroencephalogram (EEG) can improve the objectivity of a prognosis, but EEG interpretation requires clinical expertise. The algorithmic analysis of EEGs can potentially improve the accuracy and accessibility of prognoses, but existing work is limited by small and homogeneous datasets. The PhysioNet Challenge 2023 contributed to addressing these problems. It introduced the International Cardiac Arrest REsearch consortium (I-CARE) dataset, which is a large, multi-center collection of EEGs, other physiological data, and clinical outcomes, with over 57,000 hours of data from 1,020 patients from seven hospitals. It required teams to submit their complete training and inference code to improve the reproducibility and generalizability of their research. A total of 111 teams participated in the Challenge, contributing diverse approaches from academic, clinical, and industry participants worldwide.

Original language	English
Title of host publication	Computing in Cardiology, CinC 2023
Publisher	IEEE
ISBN (Electronic)	9798350382525
Publication status	Published - 2023
Event	50th Computing in Cardiology, CinC 2023 - Atlanta, United States Duration: 1 Oct 2023 → 4 Oct 2023 Conference number: 50

Publication series

Name	Computing in Cardiology
ISSN (Print)	2325-8861
ISSN (Electronic)	2325-887X

Conference

Conference	50th Computing in Cardiology, CinC 2023
Abbreviated title	CinC 2023
Country/Territory	United States
City	Atlanta
Period	1/10/23 → 4/10/23

Cite this

Reyna, M. A., Amorim, E., Sameni, R., Weigle, J., Elola, A., Rad, A. B., Seyedi, S., Kwon, H., Zheng, W. L., Ghassemi, M. M., Van Putten, M. J. A. M., Hofmeijer, J., Gaspard, N., Sivaraju, A., Herman, S. T., Lee, J. W., Westover, M. B., & Clifford, G. D. (2023). Predicting Neurological Recovery from Coma After Cardiac Arrest: The George B. Moody PhysioNet Challenge 2023. In Computing in Cardiology, CinC 2023 (Computing in Cardiology). IEEE.

@inproceedings{e5e5218ee24d465196b58a3e503913a4,

title = "Predicting Neurological Recovery from Coma After Cardiac Arrest: The George B. Moody PhysioNet Challenge 2023",

abstract = "The George B. Moody PhysioNet Challenge 2023 invited teams to develop algorithmic approaches for predicting the recovery of comatose patients after cardiac arrest. A patient's prognosis after the return of spontaneous circulation informs treatment, including the continuation or withdrawal of life support. Brain monitoring with an electroencephalogram (EEG) can improve the objectivity of a prognosis, but EEG interpretation requires clinical expertise. The algorithmic analysis of EEGs can potentially improve the accuracy and accessibility of prognoses, but existing work is limited by small and homogeneous datasets. The PhysioNet Challenge 2023 contributed to addressing these problems. It introduced the International Cardiac Arrest REsearch consortium (I-CARE) dataset, which is a large, multi-center collection of EEGs, other physiological data, and clinical outcomes, with over 57,000 hours of data from 1,020 patients from seven hospitals. It required teams to submit their complete training and inference code to improve the reproducibility and generalizability of their research. A total of 111 teams participated in the Challenge, contributing diverse approaches from academic, clinical, and industry participants worldwide.",

author = "Reyna, {Matthew A.} and Edilberto Amorim and Reza Sameni and James Weigle and Andoni Elola and Rad, {Ali Bahrami} and Salman Seyedi and Hyeokhyen Kwon and Zheng, {Wei Long} and Ghassemi, {Mohammad M.} and {Van Putten}, {Michel J.A.M.} and Jeannette Hofmeijer and Nicolas Gaspard and Adithya Sivaraju and Herman, {Susan T.} and Lee, {Jong Woo} and Westover, {M. Brandon} and Clifford, {Gari D.}",

note = "Publisher Copyright: {\textcopyright} 2023 CinC.; 50th Computing in Cardiology, CinC 2023, CinC 2023 ; Conference date: 01-10-2023 Through 04-10-2023",

year = "2023",

language = "English",

series = "Computing in Cardiology",

publisher = "IEEE",

booktitle = "Computing in Cardiology, CinC 2023",

address = "United States",

}

Reyna, MA, Amorim, E, Sameni, R, Weigle, J, Elola, A, Rad, AB, Seyedi, S, Kwon, H, Zheng, WL, Ghassemi, MM, Van Putten, MJAM , Hofmeijer, J, Gaspard, N, Sivaraju, A, Herman, ST, Lee, JW, Westover, MB & Clifford, GD 2023, Predicting Neurological Recovery from Coma After Cardiac Arrest: The George B. Moody PhysioNet Challenge 2023. in Computing in Cardiology, CinC 2023. Computing in Cardiology, IEEE, 50th Computing in Cardiology, CinC 2023, Atlanta, Georgia, United States, 1/10/23.

TY - GEN

T1 - Predicting Neurological Recovery from Coma After Cardiac Arrest

T2 - 50th Computing in Cardiology, CinC 2023

AU - Reyna, Matthew A.

AU - Amorim, Edilberto

AU - Sameni, Reza

AU - Weigle, James

AU - Elola, Andoni

AU - Rad, Ali Bahrami

AU - Seyedi, Salman

AU - Kwon, Hyeokhyen

AU - Zheng, Wei Long

AU - Ghassemi, Mohammad M.

AU - Van Putten, Michel J.A.M.

AU - Hofmeijer, Jeannette

AU - Gaspard, Nicolas

AU - Sivaraju, Adithya

AU - Herman, Susan T.

AU - Lee, Jong Woo

AU - Westover, M. Brandon

AU - Clifford, Gari D.

N1 - Conference code: 50

PY - 2023

Y1 - 2023

N2 - The George B. Moody PhysioNet Challenge 2023 invited teams to develop algorithmic approaches for predicting the recovery of comatose patients after cardiac arrest. A patient's prognosis after the return of spontaneous circulation informs treatment, including the continuation or withdrawal of life support. Brain monitoring with an electroencephalogram (EEG) can improve the objectivity of a prognosis, but EEG interpretation requires clinical expertise. The algorithmic analysis of EEGs can potentially improve the accuracy and accessibility of prognoses, but existing work is limited by small and homogeneous datasets. The PhysioNet Challenge 2023 contributed to addressing these problems. It introduced the International Cardiac Arrest REsearch consortium (I-CARE) dataset, which is a large, multi-center collection of EEGs, other physiological data, and clinical outcomes, with over 57,000 hours of data from 1,020 patients from seven hospitals. It required teams to submit their complete training and inference code to improve the reproducibility and generalizability of their research. A total of 111 teams participated in the Challenge, contributing diverse approaches from academic, clinical, and industry participants worldwide.

AB - The George B. Moody PhysioNet Challenge 2023 invited teams to develop algorithmic approaches for predicting the recovery of comatose patients after cardiac arrest. A patient's prognosis after the return of spontaneous circulation informs treatment, including the continuation or withdrawal of life support. Brain monitoring with an electroencephalogram (EEG) can improve the objectivity of a prognosis, but EEG interpretation requires clinical expertise. The algorithmic analysis of EEGs can potentially improve the accuracy and accessibility of prognoses, but existing work is limited by small and homogeneous datasets. The PhysioNet Challenge 2023 contributed to addressing these problems. It introduced the International Cardiac Arrest REsearch consortium (I-CARE) dataset, which is a large, multi-center collection of EEGs, other physiological data, and clinical outcomes, with over 57,000 hours of data from 1,020 patients from seven hospitals. It required teams to submit their complete training and inference code to improve the reproducibility and generalizability of their research. A total of 111 teams participated in the Challenge, contributing diverse approaches from academic, clinical, and industry participants worldwide.

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M3 - Conference contribution

AN - SCOPUS:85182325788

T3 - Computing in Cardiology

BT - Computing in Cardiology, CinC 2023

PB - IEEE

Y2 - 1 October 2023 through 4 October 2023

ER -

Predicting Neurological Recovery from Coma After Cardiac Arrest: The George B. Moody PhysioNet Challenge 2023

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