Research graph
References from Early prediction of in-hospital acute brain injury following cardiac arrest using machine learning: development and external validation. Local targets link to admitted publications; unresolved targets remain external evidence.
In-hospital cardiac arrest: a review
10.1001/jama.2019.1696 · 2019 · External reference
Brain injury after cardiac arrest
10.1016/s0140-6736(21)00953-3 · 2021 · External reference
Brain injury after cardiac arrest: pathophysiology, treatment, and prognosis
10.1007/s00134-021-06548-2 · 2021 · External reference
Intensive care unit mortality after cardiac arrest: the relative contribution of shock and brain injury in a large cohort
10.1007/s00134-013-3043-4 · 2013 · External reference
Standards for studies of neurological prognostication in comatose survivors of cardiac arrest: a scientific statement from the American Heart Association
10.1161/cir.0000000000000702 · 2019 · External reference
Guidelines for neuroprognostication in comatose adult survivors of cardiac arrest
10.1007/s12028-023-01688-3 · 2023 · External reference
European resuscitation council and European Society of Intensive Care Medicine guidelines 2021: post-resuscitation care
10.1016/j.resuscitation.2021.02.012 · 2021 · External reference
Regional distribution of brain injury after cardiac arrest: clinical and electrographic correlates
10.1212/wnl.0000000000013301 · 2022 · External reference
Multimodal monitoring including early EEG improves stratification of brain injury severity after pediatric cardiac arrest
10.1016/j.resuscitation.2021.06.020 · 2021 · External reference
Explainable artificial intelligence-based prediction of poor neurological outcome from head computed tomography in the immediate post-resuscitation phase
10.1038/s41598-023-32899-5 · 2023 · External reference
Electroencephalogram-based machine learning models to predict neurologic outcome after cardiac arrest: a systematic review
10.1016/j.resuscitation.2023.110049 · 2024 · External reference
Prediction model of in-hospital mortality in intensive care unit patients with cardiac arrest: a retrospective analysis of the MIMIC-IV database based on machine learning
10.1186/s12871-023-02138-5 · 2023 · External reference
A nomogram to predict in-hospital mortality in post-cardiac arrest patients: a retrospective cohort study
10.20452/pamw.16325 · 2023 · External reference
Prediction of neurologic outcome after out-of-hospital cardiac arrest: an interpretable approach with machine learning
10.1016/j.resuscitation.2024.110359 · 2024 · External reference
Unresolved reference
2017 · External reference
MIMIC-IV, a freely accessible electronic health record dataset
10.1038/s41597-022-01899-x · 2023 · External reference
Unresolved reference
2024 · External reference
An empirical assessment of SMOTE variants techniques and interpretation methods in improving the accuracy and the interpretability of student performance models
10.1007/s10639-023-12007-w · 2024 · External reference
SMOTE based class-specific extreme learning machine for imbalanced learning
10.1016/j.knosys.2019.06.022 · 2020 · External reference
A systematic method for diagnosis of hepatitis disease using machine learning
10.1007/s11334-022-00509-8 · 2023 · External reference
10.1007/978-981-19-3590-9_37
10.1007/978-981-19-3590-9_37 · External reference
Decision support system for predicting mortality in cardiac patients based on machine learning
10.3390/app13085188 · 2023 · External reference
Effective handling of missing values in datasets for classification using machine learning methods
10.3390/info14020092 · 2023 · External reference
Reinforcing artificial neural networks through traditional machine learning algorithms for robust classification of cancer
10.32604/cmc.2023.036710 · 2023 · External reference
A retrospective cohort study: predicting 90-day mortality for ICU trauma patients with a machine learning algorithm using XGBoost using MIMIC-III database
10.2147/jmdh.s416943 · 2023 · External reference
Extracting spatial effects from machine learning model using local interpretation method: an example of SHAP and XGBoost
10.1016/j.compenvurbsys.2022.101845 · 2022 · External reference
Prediction of neurologically intact survival in cardiac arrest patients without pre-hospital return of spontaneous circulation: machine learning approach
10.3390/jcm10051089 · 2021 · External reference
Cardiovascular event prediction by machine learning: the multi-ethnic study of atherosclerosis
10.1161/circresaha.117.311312 · 2017 · External reference
Scalable and accurate deep learning with electronic health records
10.1038/s41746-018-0029-1 · 2018 · External reference
Deep-learning-based out-of-hospital cardiac arrest prognostic system to predict clinical outcomes
10.1016/j.resuscitation.2019.04.007 · 2019 · External reference
The association between anemia and neurological outcome in hypoxic ischemic brain injury after cardiac arrest
10.1016/j.resuscitation.2016.12.010 · 2017 · External reference
Relationship between the hemoglobin level at hospital arrival and post-cardiac arrest neurologic outcome
10.1016/j.ajem.2011.03.031 · 2012 · External reference
Low hemoglobin levels are associated with lower cerebral saturations and poor outcome after cardiac arrest
10.1016/j.resuscitation.2015.08.015 · 2015 · External reference
The association of immediate post cardiac arrest diastolic hypertension and survival following pediatric cardiac arrest
10.1016/j.resuscitation.2019.05.033 · 2019 · External reference
A comparison of non-invasive versus invasive measures of intracranial pressure in hypoxic ischaemic brain injury after cardiac arrest
10.1016/j.resuscitation.2019.01.002 · 2019 · External reference
The impact of diastolic blood pressure values on the neurological outcome of cardiac arrest patients
10.1016/j.resuscitation.2018.07.017 · 2018 · External reference
Brain injury from cardiac arrest in children
10.1016/j.ncl.2005.10.002 · 2006 · External reference
The relationship between age and outcome in out-of-hospital cardiac arrest patients
10.1016/j.resuscitation.2015.05.015 · 2015 · External reference
Serum biomarkers of brain injury to classify outcome after pediatric cardiac arrest*
10.1097/01.ccm.0000435668.53188.80 · 2014 · External reference
The blood leukocyte count and its prognostic significance in severe head injury
10.1016/s0090-3019(01)00414-1 · 2001 · External reference
Therapeutic hypothermia impacts leukocyte kinetics after cardiac arrest
10.21037/cdt.2016.02.06 · 2016 · External reference
A new severity of illness scale using a subset of acute physiology and chronic health evaluation data elements shows comparable predictive accuracy
10.1097/ccm.0b013e31828a24fe · 2013 · External reference
Predictive value of the Oxford acute severity of illness score in acute stroke patients with stroke-associated pneumonia
10.3389/fneur.2023.1251944 · 2023 · External reference
Prognosis predictive value of the Oxford acute severity of illness score for sepsis: a retrospective cohort study
10.7717/peerj.7083 · 2019 · External reference
Arterial blood pressure during targeted temperature management after out-of-hospital cardiac arrest and association with brain injury and long-term cognitive function
10.1177/2048872619860804 · 2020 · External reference
Management of brain injury after resuscitation from cardiac arrest
10.1016/j.ncl.2008.03.015 · 2008 · External reference
The effect of different target temperatures in targeted temperature management on neurologically favorable outcome after out-of-hospital cardiac arrest: a nationwide multicenter observational study in Japan (the JAAM-OHCA registry)
10.1016/j.resuscitation.2018.10.004 · 2018 · External reference
The emergency department systolic blood pressure relationship after traumatic brain injury
10.1016/j.jss.2020.07.062 · 2021 · External reference
Traditional systolic blood pressure targets underestimate hypotension-induced secondary brain injury
10.1097/ta.0b013e31824af90b · 2012 · External reference
The association between admission systolic blood pressure and mortality in significant traumatic brain injury: a multi-Centre cohort study
10.1016/j.injury.2013.09.008 · 2014 · External reference
Evaluation of blood urea, creatinine, and glucose levels as biochemical indicators of the type and severity of traumatic brain injury
10.5137/1019-5149.jtn.29843-20.2 · 2021 · External reference
In-hospital cardiac arrest: a review
10.1001/jama.2019.1696 · ExternalCitation · doi-reference
10.1007/978-981-19-3590-9_37
10.1007/978-981-19-3590-9_37 · ExternalCitation · doi-reference
Intensive care unit mortality after cardiac arrest: the relative contribution of shock and brain injury in a large cohort
10.1007/s00134-013-3043-4 · ExternalCitation · doi-reference
Brain injury after cardiac arrest: pathophysiology, treatment, and prognosis
10.1007/s00134-021-06548-2 · ExternalCitation · doi-reference
An empirical assessment of SMOTE variants techniques and interpretation methods in improving the accuracy and the interpretability of student performance models
10.1007/s10639-023-12007-w · ExternalCitation · doi-reference
A systematic method for diagnosis of hepatitis disease using machine learning
10.1007/s11334-022-00509-8 · ExternalCitation · doi-reference
Guidelines for neuroprognostication in comatose adult survivors of cardiac arrest
10.1007/s12028-023-01688-3 · ExternalCitation · doi-reference
Relationship between the hemoglobin level at hospital arrival and post-cardiac arrest neurologic outcome
10.1016/j.ajem.2011.03.031 · ExternalCitation · doi-reference
Extracting spatial effects from machine learning model using local interpretation method: an example of SHAP and XGBoost
10.1016/j.compenvurbsys.2022.101845 · ExternalCitation · doi-reference
The association between admission systolic blood pressure and mortality in significant traumatic brain injury: a multi-Centre cohort study
10.1016/j.injury.2013.09.008 · ExternalCitation · doi-reference
The emergency department systolic blood pressure relationship after traumatic brain injury
10.1016/j.jss.2020.07.062 · ExternalCitation · doi-reference
SMOTE based class-specific extreme learning machine for imbalanced learning
10.1016/j.knosys.2019.06.022 · ExternalCitation · doi-reference
Brain injury from cardiac arrest in children
10.1016/j.ncl.2005.10.002 · ExternalCitation · doi-reference
Management of brain injury after resuscitation from cardiac arrest
10.1016/j.ncl.2008.03.015 · ExternalCitation · doi-reference
The relationship between age and outcome in out-of-hospital cardiac arrest patients
10.1016/j.resuscitation.2015.05.015 · ExternalCitation · doi-reference
Low hemoglobin levels are associated with lower cerebral saturations and poor outcome after cardiac arrest
10.1016/j.resuscitation.2015.08.015 · ExternalCitation · doi-reference
The association between anemia and neurological outcome in hypoxic ischemic brain injury after cardiac arrest
10.1016/j.resuscitation.2016.12.010 · ExternalCitation · doi-reference
The impact of diastolic blood pressure values on the neurological outcome of cardiac arrest patients
10.1016/j.resuscitation.2018.07.017 · ExternalCitation · doi-reference
The effect of different target temperatures in targeted temperature management on neurologically favorable outcome after out-of-hospital cardiac arrest: a nationwide multicenter observational study in Japan (the JAAM-OHCA registry)
10.1016/j.resuscitation.2018.10.004 · ExternalCitation · doi-reference
A comparison of non-invasive versus invasive measures of intracranial pressure in hypoxic ischaemic brain injury after cardiac arrest
10.1016/j.resuscitation.2019.01.002 · ExternalCitation · doi-reference
Deep-learning-based out-of-hospital cardiac arrest prognostic system to predict clinical outcomes
10.1016/j.resuscitation.2019.04.007 · ExternalCitation · doi-reference
The association of immediate post cardiac arrest diastolic hypertension and survival following pediatric cardiac arrest
10.1016/j.resuscitation.2019.05.033 · ExternalCitation · doi-reference
European resuscitation council and European Society of Intensive Care Medicine guidelines 2021: post-resuscitation care
10.1016/j.resuscitation.2021.02.012 · ExternalCitation · doi-reference
Multimodal monitoring including early EEG improves stratification of brain injury severity after pediatric cardiac arrest
10.1016/j.resuscitation.2021.06.020 · ExternalCitation · doi-reference
Electroencephalogram-based machine learning models to predict neurologic outcome after cardiac arrest: a systematic review
10.1016/j.resuscitation.2023.110049 · ExternalCitation · doi-reference
Prediction of neurologic outcome after out-of-hospital cardiac arrest: an interpretable approach with machine learning
10.1016/j.resuscitation.2024.110359 · ExternalCitation · doi-reference
The blood leukocyte count and its prognostic significance in severe head injury
10.1016/s0090-3019(01)00414-1 · ExternalCitation · doi-reference
Brain injury after cardiac arrest
10.1016/s0140-6736(21)00953-3 · ExternalCitation · doi-reference
MIMIC-IV, a freely accessible electronic health record dataset
10.1038/s41597-022-01899-x · ExternalCitation · doi-reference
Explainable artificial intelligence-based prediction of poor neurological outcome from head computed tomography in the immediate post-resuscitation phase
10.1038/s41598-023-32899-5 · ExternalCitation · doi-reference
Scalable and accurate deep learning with electronic health records
10.1038/s41746-018-0029-1 · ExternalCitation · doi-reference
Serum biomarkers of brain injury to classify outcome after pediatric cardiac arrest*
10.1097/01.ccm.0000435668.53188.80 · ExternalCitation · doi-reference
A new severity of illness scale using a subset of acute physiology and chronic health evaluation data elements shows comparable predictive accuracy
10.1097/ccm.0b013e31828a24fe · ExternalCitation · doi-reference
Traditional systolic blood pressure targets underestimate hypotension-induced secondary brain injury
10.1097/ta.0b013e31824af90b · ExternalCitation · doi-reference
Standards for studies of neurological prognostication in comatose survivors of cardiac arrest: a scientific statement from the American Heart Association
10.1161/cir.0000000000000702 · ExternalCitation · doi-reference
Cardiovascular event prediction by machine learning: the multi-ethnic study of atherosclerosis
10.1161/circresaha.117.311312 · ExternalCitation · doi-reference
Arterial blood pressure during targeted temperature management after out-of-hospital cardiac arrest and association with brain injury and long-term cognitive function
10.1177/2048872619860804 · ExternalCitation · doi-reference
Prediction model of in-hospital mortality in intensive care unit patients with cardiac arrest: a retrospective analysis of the MIMIC-IV database based on machine learning
10.1186/s12871-023-02138-5 · ExternalCitation · doi-reference
Regional distribution of brain injury after cardiac arrest: clinical and electrographic correlates
10.1212/wnl.0000000000013301 · ExternalCitation · doi-reference
A nomogram to predict in-hospital mortality in post-cardiac arrest patients: a retrospective cohort study
10.20452/pamw.16325 · ExternalCitation · doi-reference
Therapeutic hypothermia impacts leukocyte kinetics after cardiac arrest
10.21037/cdt.2016.02.06 · ExternalCitation · doi-reference
A retrospective cohort study: predicting 90-day mortality for ICU trauma patients with a machine learning algorithm using XGBoost using MIMIC-III database
10.2147/jmdh.s416943 · ExternalCitation · doi-reference
Reinforcing artificial neural networks through traditional machine learning algorithms for robust classification of cancer
10.32604/cmc.2023.036710 · ExternalCitation · doi-reference
Predictive value of the Oxford acute severity of illness score in acute stroke patients with stroke-associated pneumonia
10.3389/fneur.2023.1251944 · ExternalCitation · doi-reference
Decision support system for predicting mortality in cardiac patients based on machine learning
10.3390/app13085188 · ExternalCitation · doi-reference
Effective handling of missing values in datasets for classification using machine learning methods
10.3390/info14020092 · ExternalCitation · doi-reference
Prediction of neurologically intact survival in cardiac arrest patients without pre-hospital return of spontaneous circulation: machine learning approach
10.3390/jcm10051089 · ExternalCitation · doi-reference
Evaluation of blood urea, creatinine, and glucose levels as biochemical indicators of the type and severity of traumatic brain injury
10.5137/1019-5149.jtn.29843-20.2 · ExternalCitation · doi-reference
Prognosis predictive value of the Oxford acute severity of illness score for sepsis: a retrospective cohort study
10.7717/peerj.7083 · ExternalCitation · doi-reference
