Scalable RNN-Based Transfer Learning for Patient Sentiment Monitoring in Telehealth Platforms

Abstract

Telehealth platforms are generating large amounts of patient feedback that can provide valuable insights into healthcare quality. However, analyzing this unstructured text in a scalable and reliable way is challenging. In this paper, we present a sentiment analysis framework that uses a BiLSTM with an attention mechanism. The model is first trained on a large medical reviews dataset (Drugs.com) and then adapted to telehealth app reviews (Google Play) using transfer learning. Our experiments show that the model achieves 85.67% accuracy and an 84.41% macro F1-score on the medical dataset, and after transfer learning, it reaches 82.85% accuracy and an 81.95% macro F1-score on telehealth reviews. We also test the framework under different data sizes and model depths, showing that it maintains strong performance while remaining scalable. These results demonstrate the potential of our approach for real-world deployment in monitoring patient sentiment on telehealth platforms.

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