Optimizing Patient Feedback with Generative Adversarial Network Leveraging Knowledge Distillation to Improve Healthcare

Abstract

Despite progress in global healthcare systems, the utilization of domestic healthcare facilities remains limited in several regions, with a considerable proportion of people pursuing treatment overseas. This development highlights the necessity for systematic incorporation of patient-centered input, an essential element for enhancing accountability, transparency, and quality in local healthcare. Our research seeks to address this deficiency by establishing a system that collects and analyzes patient feedback to inform and improve healthcare policies and practices, particularly in areas with elevated demand for medical services. We offer an effective platform for viewing reviews from different hospitals, especially in places where people routinely visit for medical services. Therefore, we built our primary "Dhaka Private Hospitals Review Dataset," considering gathering and evaluating patient opinions methodically. We further employ transformer-based generative adversarial learning to evaluate sentiment analysis using knowledge distillation (KD) to boost model efficiency. Our proposed GANBERT architecture includes two optimized student models gated recurrent unit-based Contextualized BERT (GC-BERT) and LSTM-based Contextualized BERT (LC-BERT) with enhanced generators and discriminators. Our GC-BERT enhances execution time by 1.27% to 24.27%, while LC-BERT improves by 14.13% to 23.30%, showing superior advancements compared to other contemporary models. Each model with reductions ranging from 82.50% to 99.99% parameters, making them lightweight and efficient compared to other teacher models in the KD process. Instead of using contextual word representations which demand more space and complexity for reviewing patient feedback, we utilize the single static pretrained and low-dimensional word embedding space approach integrating student models.

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