Natural language processing for enhanced feedback mechanisms in breast cancer care: a systematic review and comparative exploration

Abstract

This systematic review and comparative analysis explores the application of Natural Language Processing (NLP) in enhancing breast cancer patient feedback mechanisms. Through a structured review of 28 relevant studies, we identified key advancements in NLP algorithms for analyzing patient feedback, including their integration with social media, online forums, and Electronic Health Records (EHRs). The findings highlight the efficacy of NLP in sentiment and emotion analysis, enabling a deeper understanding of patient experiences across diverse platforms. Advanced NLP models, particularly transformer-based architectures like BERT and GPT, demonstrate significant potential in managing complex language data and extracting actionable insights from unstructured patient feedback. However, challenges such as data integration, noise management, and bias mitigation remain, indicating areas for continued methodological refinement. The reviewed studies collectively underscore the progress and potential of NLP in augmenting breast cancer care by mining various data sources for patient feedback. They emphasize the diversity of approaches required to address the multifaceted nature of breast cancer care, from sentiment analysis to clinical insights derived from EHRs. These advancements pave the way for more personalized and informed breast cancer care practices, as demonstrated by the strides in extracting and analyzing patient feedback through advanced computational methods. This study contributes to the growing body of knowledge on NLP applications in healthcare, highlighting its transformative potential to improve patient-centered care and inform clinical decision-making in breast cancer treatment.

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