HCAT: Advancing Unstructured Healthcare Data Analysis Through Hierarchical and Context-Aware Mechanisms

Abstract

To that end, this study presents the Hierarchical Context-Aware Transformer (HCAT), a new model to perform analysis on unstructured healthcare data that resolves significant problems related to medical text. In the proposed model, the hierarchical structure of the system is integrated with the context-sensitive mechanisms to process the healthcare documents at sentence level and document levels. HCAT complies with domain knowledge by a specific attention module and uses a detailed loss function that focuses on classification accuracy besides encouraging domain adaptation. The quantitative experiment shows that HCAT is a better choice than Bi-LSTM and BERT for sentence representation. The model attains 92.30% test accuracy on medical text classification, conversing with high computational efficiency; batch processing time is about 150ms, while the memory consumed is 320 MB. The proposed architecture for clinical text representation facilitates the incorporation of long-range dependencies for clinical story representation, whereas the context-sensitive layer supports a better understanding of medical language. Precision and recall are significant because of the healthcare application of the model; the model has an accuracy of 91.8% and a recall of 93.2%. From these results, it can be concluded that HCAT presented significant progress in computing healthcare data. It provides a highly practical application for real-world extraction of medical data from unformatted text.