Artificial neural networks improve early outcome prediction and risk classification in out-of-hospital cardiac arrest patients admitted to intensive care

J Johnsson, O Björnsson, P Andersson, A Jakobsson… - Critical care, 2020 - Springer
Background Pre-hospital circumstances, cardiac arrest characteristics, comorbidities and
clinical status on admission are strongly associated with outcome after out-of-hospital …

Predicting neurological outcome after out-of-hospital cardiac arrest with cumulative information; development and internal validation of an artificial neural network …

P Andersson, J Johnsson, O Björnsson, T Cronberg… - Critical Care, 2021 - Springer
Background Prognostication of neurological outcome in patients who remain comatose after
cardiac arrest resuscitation is complex. Clinical variables, as well as biomarkers of brain …

Machine learning algorithm to predict mortality in patients undergoing continuous renal replacement therapy

MW Kang, J Kim, DK Kim, KH Oh, KW Joo, YS Kim… - Critical Care, 2020 - Springer
Background Previous scoring models such as the Acute Physiologic Assessment and
Chronic Health Evaluation II (APACHE II) and the Sequential Organ Failure Assessment …

Do patient characteristics or factors at resuscitation influence long-term outcome in patients surviving to be discharged following inhospital cardiac arrest?

M Skrifvars, M Castren, J Nurmi, A Thorén, S Aune… - Critical Care, 2007 - Springer
Methods An analysis of IHCA data collected from one Swedish tertiary hospital and from five
Finnish secondary hospitals over a 10-year period. The study was limited to patients …

Comparison between logistic regression and neural networks to predict death in patients with suspected sepsis in the emergency room

F Jaimes, J Farbiarz, D Alvarez, C Martínez - Critical care, 2005 - Springer
Introduction Neural networks are new methodological tools based on nonlinear models.
They appear to be better at prediction and classification in biological systems than do …

Predicting outcome in patients with moderate to severe traumatic brain injury using electroencephalography

ME Haveman, MJAM Van Putten, HW Hom… - Critical care, 2019 - Springer
Background Better outcome prediction could assist in reliable quantification and
classification of traumatic brain injury (TBI) severity to support clinical decision-making. We …

Prediction of cardiac arrest in critically ill patients presenting to the emergency department using a machine learning score incorporating heart rate variability …

MEH Ong, CH Lee Ng, K Goh, N Liu, ZX Koh… - Critical Care, 2012 - Springer
Introduction A key aim of triage is to identify those with high risk of cardiac arrest, as they
require intensive monitoring, resuscitation facilities, and early intervention. We aim to …

Decision-tree model for predicting outcomes after out-of-hospital cardiac arrest in the emergency department

Y Goto, T Maeda, Y Goto - Critical Care, 2013 - Springer
Introduction Estimation of outcomes in patients after out-of-hospital cardiac arrest (OHCA)
soon after arrival at the hospital may help clinicians guide in-hospital strategies, particularly …

External validation of the 2020 ERC/ESICM prognostication strategy algorithm after cardiac arrest

CS Youn, KN Park, SH Kim, BK Lee, T Cronberg… - Critical Care, 2022 - Springer
Purpose To assess the performance of the post-cardiac arrest (CA) prognostication strategy
algorithm recommended by the European Resuscitation Council (ERC) and the European …

Prediction of death and prolonged mechanical ventilation in acute lung injury

O Gajic, B Afessa, BT Thompson, F Frutos-Vivar… - Critical care, 2007 - Springer
Introduction Prediction of death and prolonged mechanical ventilation is important in terms
of projecting resource utilization and in establishing protocols for clinical studies of acute …