viTBI-BERT: A Vietnamese Language Model for Prediction of Traumatic Brain Injury

Abstract

Artificial Intelligence (AI) plays an increasingly significant role in predicting diseases by leveraging large-scale and complex datasets. AI-based traumatic brain injury (TBI) prediction has traditionally relied on multimodal data, including both structured information like test results and unstructured data such as CT or/and MRI scans. However, one important source of information—physician conclusions in text form at the time of admission and discharge—has largely been underutilized. These textual conclusions offer valuable insights into the patient's condition. This paper proposes a novel approach to classifying TBI severity using physicians' written conclusions. We introduce the viTBI-BERT model, which is built on the ViHealthBERT backbone. Our approach includes pre-processing techniques such as acronym normalization, special character removal, and noun chunking and textual data augmentation. After pre-processing step, the data is passed through the pre-trained ViHealthBERT model, with an additional fully connected layer and a softmax layer for the classification task. The model is tested on three sets of medical notes derived from a self-collected dataset consisting of clinical and CT findings from 503 Vietnamese patients. The viTBI-BERT model achieved a highest sensitivity of 71% for classifying four levels of injury severity. These findings demonstrate the potential of language-based analysis, which, when combined with structured clinical and subclinical data, could further enhance TBI classification outcomes.

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