Daya MR, Schmicker RH, Zive DM, et al. Out-of-hospital cardiac arrest survival improving over time: results from the resuscitation outcomes consortium (ROC). Resuscitation. 2015;91:108–15.
Article
Google Scholar
Govindarajan P, Lin L, Landman A, McMullan JT, McNally BF, Crouch AJ, Sasson C. Practice variability among the EMS systems participating in cardiac arrest registry to enhance survival (CARES). Resuscitation. 2012;83(1):76–80.
Article
Google Scholar
Coppler PJ, Sawyer KN, Youn CS, et al. Variability of post-cardiac arrest care practices among cardiac arrest centers: United States and South Korean dual network survey of emergency physician research principal investigators. Ther Hypothermia Temp Manag. 2017;7(1):30–5. https://doi.org/10.1089/ther.2016.0017.
Article
PubMed
PubMed Central
Google Scholar
Wiens J, Shenoy ES. Machine learning for healthcare: on the verge of a major shift in healthcare epidemiology. Clin Infect Dis. 2018;66(1):149–53.
Article
Google Scholar
Ngiam KY, Khor IW. Big data and machine learning algorithms for health-care delivery. Lancet Oncol. 2019;20(5):e262–73.
Article
Google Scholar
Beam AL, Kohane IS. Big data and machine learning in health care. JAMA. 2018;319(13):1317–8.
Article
Google Scholar
Giger ML. Machine learning in medical imaging. J Am College Radiol. 2018;15(3):512–20.
Article
Google Scholar
Ardabili SF, Mosavi A, Ghamisi P, Ferdinand F, Varkonyi-Koczy AR, Reuter U, Rabczuk T, Atkinson PM. Covid-19 outbreak prediction with machine learning. Available at SSRN 3580188. 2020.
Vamathevan J, Clark D, Czodrowski P, Dunham I, Ferran E, Lee G, Li B, Madabhushi A, Shah P, Spitzer M, Zhao S. Applications of machine learning in drug discovery and development. Nat Rev Drug Discov. 2019;18(6):463–77.
Article
CAS
Google Scholar
Sharabiani A, Darabi H, Bress A, Cavallari L, Nutescu E, Drozda K. Machine learning based prediction of warfarin optimal dosing for African American patients. In: 2013 IEEE international conference on automation science and engineering (CASE) 2013; pp 623–628. IEEE.
Darabi H, Galanter WL, Lin JY, Buy U, Sampath R. Modeling and integration of hospital information systems with Petri nets. In: 2009 IEEE/INFORMS international conference on service operations, logistics and informatics 2009, pp 190–195. IEEE.
Haji M, Darabi H. A simulation case study: Reducing outpatient waiting time of otolaryngology care services using VBA. In: 2011 IEEE international conference on automation science and engineering 2011, pp 525–530. IEEE.
Blomberg SN, Folke F, Ersbøll AK, Christensen HC, Torp-Pedersen C, Sayre MR, Counts CR, Lippert FK. Machine learning as a supportive tool to recognize cardiac arrest in emergency calls. Resuscitation. 2019;138:322–9.
Article
Google Scholar
Majumder AK, ElSaadany YA, Young R, Ucci DR. An energy efficient wearable smart IoT system to predict cardiac arrest. Adv Hum-Comput Interact. 2019. https://doi.org/10.1155/2019/1507465.
Article
Google Scholar
Kwon JM, Lee Y, Lee Y, Lee S, Park J. An algorithm based on deep learning for predicting in-hospital cardiac arrest. J Am Heart Assoc. 2018;7(13):e008678.
Article
Google Scholar
Krizmaric M, Verlic M, Stiglic G, Grmec S, Kokol P. Intelligent analysis in predicting outcome of out-of-hospital cardiac arrest. Comput Methods Progr Biomed. 2009;95(2 Suppl):S22-32.
Article
Google Scholar
Nanayakkara S, Fogarty S, Tremeer M, et al. Characterising risk of in-hospital mortality following cardiac arrest using machine learning: a retrospective international registry study. PLoS Med. 2018;15(11):e1002709.
Article
Google Scholar
Region 11 Chicago EMS—Region 11 Chicago EMS. 21 September 2020. https://chicagoems.org.
REGION XI CHICAGO EMS SYSTEM POLICIES AND PROCEDURES, 2017, https://chicagoems.org/wp-content/uploads/sites/2/2017/08/2017-PP_APPROVED.pdf.
MyCares. 21 September 2020, https://mycares.net/
McNally B, Stokes A, Crouch A, Kellermann AL, CARES Surveillance Group. CARES: cardiac arrest registry to enhance survival. Ann Emerg Med. 2009;54(5):674–83.
Potdar K, Pardawala TS, Pai CD. A comparative study of categorical variable encoding techniques for neural network classifiers. Int J Comput Appl. 2017;175(4):7–9.
Google Scholar
Reynolds JC, Callaway CW, El Khoudary SR, Moore CG, Alvarez RJ, Rittenberger JC. Coronary angiography predicts improved outcome following cardiac arrest: propensity-adjusted analysis. J Intensive Care Med. 2009;24:179–86.
Article
Google Scholar
Dumas F, Cariou A, Manzo-Silberman S, Grimaldi D, Vivien B, Rosencher J, Empana JP, Carli P, Mira JP, Jouven X, Spaulding C. Immediate percutaneous coronary intervention is associated with better survival after out-of-hospital cardiac arrest: insights from the PROCAT (Parisian Region Out of hospital Cardiac ArresT) registry. Circul: Cardiovasc Interv. 2010;3(3):200–7.
Google Scholar
Hollenbeck RD, McPherson JA, Mooney MR, Unger BT, Patel NC, McMullan PW Jr, Hsu CH, Seder DB, Kern KB. Early cardiac catheterization is associated with improved survival in comatose survivors of cardiac arrest without STEMI. Resuscitation. 2014;85:88–95.
Article
Google Scholar
Grossestreuer AV, Abella BS, Sheak KR, et al. Inter-rater reliability of post-arrest cerebral performance category (CPC) scores. Resuscitation. 2016;109:21–4.
Article
Google Scholar
Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu TY. Lightgbm: A highly efficient gradient boosting decision tree. In: Advances in neural information processing systems 2017, pp 3146–3154.
Chen T, Guestrin C. Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining 2016, pp 785–794.
Safavian SR, Landgrebe D. A survey of decision tree classifier methodology. IEEE Trans Syst Man Cybern. 1991;21(3):660–74.
Article
Google Scholar
Liaw A, Wiener M. Classification and regression by random Forest. R news. 2002;2(3):18–22.
Google Scholar
Peter S, Diego F, Hamprecht FA, Nadler B. Cost efficient gradient boosting. In: Advances in neural information processing systems 2017, pp 1551–1561.
Peterson LE. K-nearest neighbor. Scholarpedia. 2009;4(2):1883.
Article
Google Scholar
Hosmer DW Jr, Lemeshow S, Sturdivant RX. Applied logistic regression. Hoboken: Wiley; 2013.
Book
Google Scholar
Yu H, Kim S. SVM tutorial-classification, regression and ranking. Handb Natural Comput. 2012;1:479–506.
Article
Google Scholar
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–44.
Article
CAS
Google Scholar
Harford S, Darabi H, Del Rios M, Majumdar S, Karim F, Hoek TV, Erwin K, Watson DP. A machine learning based model for out of hospital cardiac arrest outcome classification and sensitivity analysis. Resuscitation. 2019;138:134–40.
Article
Google Scholar
Jia Y, Zhang Y, Weiss R, Wang Q, Shen J, Ren F, Nguyen P, Pang R, Moreno IL, Wu Y. Transfer learning from speaker verification to multispeaker text-to-speech synthesis. In: Advances in neural information processing systems 2018, pp 4480–4490.
Chicco D, Jurman G. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics. 2020;21(1):1–3.
Article
Google Scholar
Roulston MS. Performance targets and the Brier score. Meteorol Appl: J Forecast Pract Appl Train Tech Model. 2007;14(2):185–94.
Article
Google Scholar
Sundararajan M, Najmi A. The many Shapley values for model explanation. In: International conference on machine learning 2020, pp 9269–9278. PMLR.
Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380(14):1347–58.
Article
Google Scholar
Al-Dury N, Ravn-Fischer A, Hollenberg J, Israelsson J, Nordberg P, Strömsöe A, Axelsson C, Herlitz J, Rawshani A. Identifying the relative importance of predictors of survival in out of hospital cardiac arrest: a machine learning study. Scand J Trauma Resusc Emerg Med. 2020;28(1):1–8.
Article
Google Scholar
Kwon JM, Jeon KH, Kim HM, Kim MJ, Lim S, Kim KH, Song PS, Park J, Choi RK, Oh BH. Deep-learning-based out-of-hospital cardiac arrest prognostic system to predict clinical outcomes. Resuscitation. 2019;139:84–91. https://doi.org/10.1016/j.resuscitation.2019.04.007.
Article
PubMed
Google Scholar
Callaway CW, Schmicker R, Kampmeyer M, et al. Receiving hospital characteristics associated with survival after out-of-hospital cardiac arrest. Resuscitation. 2010;81(5):524–9.
Article
Google Scholar
Carr BG, Kahn JM, Merchant RM, et al. Inter-hospital variability in post-cardiac arrest mortality. Resuscitation. 2009;80(1):30–4.
Article
Google Scholar
Schober A, Sterz F, Laggner AN, et al. Admission of out-of-hospital cardiac arrest victims to a high volume cardiac arrest center is linked to improved outcome. Resuscitation. 2016;106:42–8.
Article
Google Scholar
Blum N, Del Rios M, Kotini P, Nguyen H, Campbell T, Markul E, Weber J, Vanden HT. Interhospital variability in out-of-hospital cardiac arrest treatment and survival in a large metropolitan Aea. Acad Emerg Med. 2019;26(S1):A353.
Google Scholar