JMIR Publications

ABSTRACT

Background:

Currently, the treatment and care of patients with traumatic brain injury (TBI) is an intractable health problem worldwide and greatly increases the medical burden in society. However, machine learning-based algorithms and the use of a large amount of data accumulated in the clinic in the past can predict the hospitalization time of brain injury patients in advance, so as to design a reasonable arrangement of resources and effectively reduce the medical burden of society. Especially in China, where medical resources are so tight, this method has important application value.

Objective:

We aimed to develop a system based on a machine learning model for predicting the length of hospitalization of patients with traumatic brain injury, which is available to patients, nurses, and physicians.

Methods:

We collected information about patients treated at the Neurosurgery Center of the Second Affiliated Hospital of Anhui Medical University during May 2017-May 2022, and we trained and tested the machine learning model using five cross-validations to avoid overfitting; 28 types of independent variables were used as input variables in the machine learning model, and the length of hospitalization was used as the output variables. Once the models were trained, we obtained the error and goodness of fit(R2) of each machine learning model from the 5 rounds of cross-validation and compared them to select the best predictive model to be encapsulated in the developed system. In addition, we externally tested the models using clinical data related to patients treated at the First Affiliated Hospital of Anhui Medical University from June 2021-February 2022.

Results:

Six machine learning models were built, including support vector regression machine, convolutional neural network, BP neural network, random forest, logistic regression, and multilayer perceptron. Among them, the support vector regression has the smallest error of 10.22% on the test set, the highest goodness-of-fit of 90.4%, all performances are the best among the six models. In addition, we used external data sets to verify the experimental results of these six models in order to avoid experimental chance, and the support vector regression machine eventually performed the best in the external data sets. Therefore, we chose to encapsulate the support vector regression machine into our system for predicting the length of stay of patients with traumatic brain trauma. Finally, we made the developed system available to patients, nurses, and physicians, and the satisfaction questionnaire showed that patients, nurses, and physicians agreed that the system was effective in providing clinical decisions to help patients, nurses, and physicians.

Conclusions:

This study shows that the support vector regression machine model developed using machine learning methods can accurately predict the length of hospitalization of patients with traumatic brain injury, and the developed prediction system has strong clinical utility.