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Traumatic Brain Injury l: Introduction

Traumatic Brain Injury l: Introduction

DefinitionTraumatic brain injury, or TBI, is a disturbance of normal brain function induced by an external mechanical force, such as a direct blow to the head or a penetrating injury. It can affect both brain structure and function, producing a wide range of clinical outcomes. TBI is a heterogeneous condition, meaning its effects may differ based on the type, location, and severity of the injury.Basis of ClassificationTBI is classified based on severity, injury mechanism, or pathophysiology. In...

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Controlled Cortical Impact Model for Traumatic Brain Injury

Controlled Cortical Impact Model for Traumatic Brain Injury

Published on: August 5, 2014

AI-based models to predict decompensation on traumatic brain injury patients.

Ricardo Ribeiro¹, Inês Neves², Hélder P Oliveira¹

¹INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal; FCUP - Faculty of Sciences of the University of Porto, Porto, Portugal.

Computers in Biology and Medicine

|January 9, 2025

Summary

Artificial Intelligence (AI) models can predict patient decompensation in Traumatic Brain Injury (TBI) cases using Electronic Health Records (EHR). This approach aids clinical decisions and improves care for TBI patients in the ICU.

Keywords:

Decompensation prediction Deep learning Electronic health records data Traumatic brain injury

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Last Updated: May 6, 2026

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Published on: August 5, 2014

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Published on: May 27, 2022

Assessing Changes in Synaptic Plasticity Using an Awake Closed-Head Injury Model of Mild Traumatic Brain Injury

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Published on: January 20, 2023

Area of Science:

Medical Informatics
Neurology
Artificial Intelligence

Background:

Traumatic Brain Injury (TBI) poses significant mortality risks, reaching 30-40% in severe cases.
Effective clinical decision-making for TBI complications remains a challenge.

Purpose of the Study:

To develop and evaluate AI models for predicting TBI patient decompensation.
To enhance patient care and clinical decision-making using data-driven approaches.

Main Methods:

Utilized sequential data from Electronic Health Records (EHR) of 2261 TBI patients from the MIMIC-III dataset.
Employed Logistic Regression (LR), Long-short term memory (LSTM), and Transformers architectures for prediction.
Explored feature sets, missing data imputation, and data imbalance techniques.

Main Results:

LSTM models achieved high performance, with AUROC scores of 0.918 (original features) and 0.929 (added features with class weights).
The study demonstrated the viability of using EHR time series data for predictive modeling.

Conclusions:

LSTM models using EHR time series data are effective for predicting TBI patient decompensation.
This predictive capability can serve as an early indicator for necessary clinical interventions.