AIMC Topic: Glasgow Coma Scale
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Is artificial intelligence superior to traditional regression methods in predicting prognosis of adult traumatic brain injury?
Neurosurgical review
Apr 7, 2025
Traumatic brain injury (TBI) is a significant global health issue with high morbidity and mortality rates. Recent studies have shown that machine learning algorithms outperform traditional logistic regression models in predicting functional outcomes ...
System for Predicting Neurological Outcomes Following Cardiac Arrest Based on Clinical Predictors Using a Machine Learning Method: The Neurological Outcomes After Cardiac Arrest Method.
Neurocritical care
Feb 20, 2025
BACKGROUND: A multimodal approach may prove effective for predicting clinical outcomes following cardiac arrest (CA). We aimed to develop a practical predictive model that incorporates clinical factors related to CA and multiple prognostic tests usin...
Development of clinical decision support for patients older than 65 years with fall-related TBI using artificial intelligence modeling.
PloS one
Feb 3, 2025
BACKGROUND: Older persons comprise most traumatic brain injury (TBI)-related hospitalizations and deaths and are particularly susceptible to fall-induced TBIs. The combination of increased frailty and susceptibility to clinical decline creates a sign...
The use of cloud based machine learning to predict outcome in intracerebral haemorrhage without explicit programming expertise.
Neurosurgical review
Dec 3, 2024
Machine Learning (ML) techniques require novel computer programming skills along with clinical domain knowledge to produce a useful model. We demonstrate the use of a cloud-based ML tool that does not require any programming expertise to develop, val...
Development and external validation of an interpretable machine learning model for the prediction of intubation in the intensive care unit.
Scientific reports
Nov 8, 2024
Given the limited capacity to accurately determine the necessity for intubation in intensive care unit settings, this study aimed to develop and externally validate an interpretable machine learning model capable of predicting the need for intubation...
Development of predictive model for the neurological deterioration among mild traumatic brain injury patients using machine learning algorithms.
Neurosurgical review
Aug 28, 2024
BACKGROUND: Mild traumatic brain injury (mTBI) comprises a majority of traumatic brain injury (TBI) cases. While some mTBI would suffer neurological deterioration (ND) and therefore have poorer prognosis. This study was designed to develop the predic...
Phenotypes of Patients with Intracerebral Hemorrhage, Complications, and Outcomes.
Neurocritical care
Aug 6, 2024
BACKGROUND: The objective of this study was to define clinically meaningful phenotypes of intracerebral hemorrhage (ICH) using machine learning.
Enhancing Outcome Prediction in Intracerebral Hemorrhage Through Deep Learning: A Retrospective Multicenter Study.
Academic radiology
Aug 1, 2024
RATIONALE AND OBJECTIVES: This study aimed to employ deep learning techniques to analyze and validate an automatic prognostic biomarker for predicting outcomes following intracerebral hemorrhage (ICH).
Predicting the Severity and Discharge Prognosis of Traumatic Brain Injury Based on Intracranial Pressure Data Using Machine Learning Algorithms.
World neurosurgery
Mar 20, 2024
OBJECTIVE: This study aimed to explore the potential of employing machine learning algorithms based on intracranial pressure (ICP), ICP-derived parameters, and their complexity to predict the severity and short-term prognosis of traumatic brain injur...
Upper gastrointestinal haemorrhage patients' survival: A causal inference and prediction study.
European journal of clinical investigation
Feb 20, 2024
BACKGROUND: Upper gastrointestinal (GI) bleeding is a common medical emergency. This study aimed to develop models to predict critically ill patients with upper GI bleeding in-hospital and 30-day survival, identify the correlation factor and infer th...
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