Emotion-Aware Healthcare Chatbots Using Multimodal Deep Learning and Natural Language Understanding

2025-11-28T05:28:22+00:00

The advent of modern technologies like Artificial Intelligence (AI), Internet of Things (IoT) and Deep Learning (DL) has ushered in a transformative era in healthcare, offering innovative solutions towards personalized healthcare by enhancing the quality of various medical services. Our proposed methodology involves the development of a BERT-based medical chatbot, leveraging cutting-edge deep learning technology to significantly enhance healthcare communication and accessibility. The traditional challenges faced by medical chatbots, such as imprecise understanding of medical conversations, inaccurate responses to jargon, and the inability to offer personalized feedback, are addressed through the utilization of Bidirectional Encoder Representations from Transformers (BERT). The performance metrics of our chatbot underscores its effectiveness. With an accuracy of 98%, the chatbot ensures a high level of precision in handling medical queries. The precision score of 97% attests to the accuracy and reliability of its responses. The AUC-ROC score of 97% indicates the chatbot's exceptional ability to predict specific diseases based on user queries and symptoms, showcasing its robust predictive power. Furthermore, a recall of 96% demonstrates the chatbot's capability to avoid missing cases in medical diagnoses, ensuring comprehensive coverage of potential conditions. The F1 score of 98% showcases the chatbot's proficiency in delivering accurate and personalized healthcare information, striking a harmonious balance between precision and recall. Our BERT-based medical chatbot not only addresses the limitations of traditional approaches but also achieves a remarkable performance with high accuracy, precision, predictive power, and comprehensive coverage, making it a valuable tool for advancing the quality of healthcare services.

A Study on the Empowering Effect of Digital Economy Industrial Policies on Common Prosperity in China's Urban and Rural Areas

2025-12-04T18:01:36+00:00

Guided by the goal of common prosperity, China's urban-rural income gap has narrowed continuously but remains significantly wide. With the intensive implementation of policies such as the "Overall Plan for Building a Digital China," the digital economy has emerged as a new engine driving urban-rural integration. Using panel data from 283 prefecture-level cities in China from 2012 to 2023, this study empirically analyzes the intrinsic relationship and operational mechanisms between digital economy industrial policies and common prosperity in urban and rural areas. The results indicate that the enabling effect of digital economy industrial policies on urban-rural common prosperity exhibits significant regional and digital transformation heterogeneity. Specifically, these policies demonstrate stronger enabling effects in the eastern, central, and northeastern regions and areas with high levels of digital transformation. Mechanism analysis indicates that digital economy industrial policies drive shared prosperity through two pathways: stimulating entrepreneurial activity and enhancing resource allocation efficiency. Based on these findings, this study proposes a three-pronged policy approach: optimizing the digital industrial ecosystem through dual measures, channeling entrepreneurial vitality to precisely nurture the fertile ground of shared prosperity, and pursuing optimal resource allocation solutions through coordinated efforts. These recommendations aim to provide theoretical insights for advancing shared prosperity between urban and rural areas.

Al-Qanṭara

Emotion-Aware Healthcare Chatbots Using Multimodal Deep Learning and Natural Language Understanding

A Study on the Empowering Effect of Digital Economy Industrial Policies on Common Prosperity in China's Urban and Rural Areas