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Comparing Prediction of Early TBI Mortality with Multilayer Perceptron Neural Network and Convolutional Neural

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|September 10, 2022

Summary

This study predicts 14-day mortality in traumatic brain injury (TBI) patients using machine learning. Convolutional networks achieved high accuracy, offering a valuable tool for early mortality prediction in TBI.

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Systems Analysis of the Neuroinflammatory and Hemodynamic Response to Traumatic Brain Injury

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Area of Science:

Medical Informatics
Machine Learning
Neurology

Background:

Traumatic brain injury (TBI) poses a significant global health challenge, particularly in low- and middle-income countries.
Accurate and early prediction of mortality in TBI patients is crucial for resource allocation and patient management.

Purpose of the Study:

To compare the performance of multilayer perceptron neural networks and convolutional neural networks for predicting 14-day mortality in TBI patients.
To evaluate machine learning models using a dataset from a low- and middle-income country.

Main Methods:

Utilized a dataset of 529 TBI patients with 16 predictor variables.
Employed imputation techniques including decision tree, random forest, k-nearest-neighbor, and linear regression for missing data.
Trained and simulated neural networks using optimization methods like RMSProp, Adam, Adamax, and SGDM.

Main Results:

Achieved a prediction accuracy of 0.845.
Obtained an area under the Receiver Operating Characteristic (ROC) curve of 0.911, indicating strong predictive performance.
Convolutional networks demonstrated superior performance in predicting 14-day mortality.

Conclusions:

Machine learning models, particularly convolutional networks, can effectively predict early mortality in TBI patients.
The developed models show high clinical relevance with an AUC of 0.911 for early mortality prediction in TBI.
This approach offers a promising tool for improving patient outcomes in resource-limited settings.