Explainable machine learning model based on EEG, ECG, and clinical features for predicting neurological outcomes in cardiac arrest patient

Early and accurate prediction of neurological outcomes in comatose patients following cardiac arrest is critical for informed clinical decision-making. Existing studies have predominantly focused on EEG for assessing brain injury, with some exploring ECG data. However, the integration of EEG, ECG, a...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Scientific reports Ročník 15; číslo 1; s. 11498 - 11
Hlavní autoři:	Niu, Yanxiang, Chen, Xin, Fan, Jianqi, Liu, Chunli, Fang, Menghao, Liu, Ziquan, Meng, Xiangyan, Liu, Yanqing, Lu, Lu, Fan, Haojun
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Nature Publishing Group UK 03.04.2025 Nature Publishing Group Nature Portfolio
Témata:	631/378 692/4019 Accuracy Aged Brain injury Cardiac arrest Coma Coma - diagnosis Coma - etiology Decision making EEG Electrocardiography - methods Electroencephalography - methods Female Heart Heart Arrest - complications Heart Arrest - diagnosis Heart Arrest - physiopathology Heart rate variability Humanities and Social Sciences Humans Learning algorithms Machine Learning Male Middle Aged Multi-modal evaluation multidisciplinary Prognosis Quantitative EEG Regression analysis ROC Curve Science Science (multidisciplinary) SHAP analysis Quantitative EEG Multi-modal evaluation SHAP analysis Heart rate variability Cardiac arrest Machine learning
ISSN:	2045-2322, 2045-2322
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!