Evaluation of optimal scene time interval for out-of-hospital cardiac arrest using a deep neural network

Artificial intelligence to support out-of-hospital cardiac arrest care: A scoping review

“…The majority were from 2022 (5/15, 34%) 8 , 9 , 10 , 11 , 12 and all but one were published in the past five years. Most studies were carried out by multinational collaborations (4/15, 27%) 12 , 13 , 14 , 15 and from Korea (3/15, 20%) 16 , 17 , 18 , Japan (2/15, 14%) 9 , 19 , and the United States (2/15, 14%) 8 , 20 . Eleven studies (73%) focused on survival prediction 13 , 14 , 8 , 9 , 10 , 11 , 17 , 18 , 19 , 20 , 21 , two studies (13%) focused on ROSC prediction 12 , 15 , and two studies (13%) focused on both 16 , 22 .…”

“…Most studies were carried out by multinational collaborations (4/15, 27%) 12 , 13 , 14 , 15 and from Korea (3/15, 20%) 16 , 17 , 18 , Japan (2/15, 14%) 9 , 19 , and the United States (2/15, 14%) 8 , 20 . Eleven studies (73%) focused on survival prediction 13 , 14 , 8 , 9 , 10 , 11 , 17 , 18 , 19 , 20 , 21 , two studies (13%) focused on ROSC prediction 12 , 15 , and two studies (13%) focused on both 16 , 22 . Most studies excluded patients < 18 years of age or did not specify an age cutoff, while a single study by Al-Dury et al 13 explicitly included both adults and children.…”

“…Among studies with models aimed at predicting survival and/or ROSC, eleven utilized ML 17 , 19 , 9 , 10 , 11 , 13 , 14 , 15 , 21 , 22 , 23 and four used both ML and DL 8 , 16 , 18 , 20 . All of these studies utilized a random forest classification algorithm as one type of the ML classification algorithms and additional ML and DL classification methods included logistic regression 8 , 9 , 16 , 18 , 19 , 20 , 21 , support vector machine 9 , 18 , 19 , 20 , 22 , k-nearest neighbor 8 , 9 , 16 , 20 , 22 , decision tree 8 , 9 , 16 , 20 , light gradient boosted machine (LightGBM) 8 , 9 , 14 , 20 , extreme gradient boosting (XGboost) 8 , 9 , 17 , 20 , multilayer perceptron 18 , 19 , 21 , Autoscore 10 , 15 , deep neural networks 8 , 20 , embedded fully convolutional network 8 , 20 , gradient boosting 8 , 20 , artificial neural networks 14 , classification and regression tree 11 , Lagrangian support vector machine 16 , naïve bayes 22 , ne...…”

“…4 ). In these studies, 12 of 15 models 12 , 13 , 8 , 9 , 10 , 15 , 16 , 17 , 19 , 20 , 21 , 22 utilized only predictors from the prehospital setting, while the remaining three models 12 , 14 , 18 also incorporated one to two predictor variables obtained upon ED arrival including initial ED vitals, initial ED ECG, consciousness level upon ED arrival, and time from ED arrival to ROSC.

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“…Most of these studies utilized classification algorithms that were developed and validated with data from EMS systems in Asia. 10 , 11 , 13 , 14 , 15 , 16 , 17 , 18 , 19 It is known that EMS systems vary widely both at the international level and within the United States. 62 As such, these models may not be applicable within the North American and European EMS systems.…”