SENTICARE-GPT: A cross-fused generative transformer for emotion-aware and explainable sentiment analysis of patient feedback

In today's healthcare environment, understanding patient emotion is an invaluable measure for evaluating service quality, finding significant loopholes, and improving therapeutic outcomes. Yet, recently devised transformer-based models, such as GPT-3.5, Health-LLM, and Polaris, while brimming with promise for advancing clinical NLP conceptually, are frequently unable to handle the kind of terminology, negations, and simple statements that are ubiquitous in a domain, let alone clear and instantaneous explanations. Such models are too inefficient for deployment in actual healthcare system settings, lack sophisticated emotional reasoning, and are often poor performers when dealing with ambiguous input. Enter SENTICARE-GPT, a novel model for patient sentiment analysis that emerges from the combination of clinical natural language processing, emotion detection, and generative transformers. It starts from a Clinical Feedback Preprocessor (CFP+), which expands abbreviations and remembers context-sensitive phrases while normalizing medical language. Further, boasts of a Cross-Fused GPT Decoder (CF-GPT) with Dual Cross Attention that consolidates the semantic and affective signals every transformer layer. The Contrastive Clinical Sentiment Adapter (CCSA) mentioned here aids in categorization under ambiguous feedback by a mere recognition of short sentiment changes. As a multi-agent classifier, the Sentiment Intensity & Risk Estimator (SIRE) forecasts urgency level, emotional intensity, and polarity. Finally, the Explainable Sentiment Tracer (X-ST) permits healthcare professionals to make transparent sentiment decisions by providing phrase-level justifications based on integrated gradients and generating justification modules. The experimental evaluations show that SENTICARE-GPT is very powerful for real-time interpretable healthcare sentiment analysis, ranking higher than existing state-of-the-art models in sentiment accuracy (91.6%), urgency recall (90.7%), and explainability satisfaction (4.6/5).