원문정보
초록
영어
This study explores the integration of Explainable Artificial Intelligence (XAI) and network science in healthcare, focusing on enhancing healthcare data interpretation and improving diagnostic and treatment methods. Key methodologies like Graph Neural Networks, Community Detection, Overlapping Network Models, and Time-Series Network Analysis are examined in depth for their potential in patient health management. The research highlights the transformative role of XAI in making complex AI models transparent and interpretable, essential for accurate, data-driven decision-making in healthcare. Case studies demonstrate the practical application of these methodologies in predicting diseases, understanding drug interactions, and tracking patient health over time. The study concludes with the immense promise of these advancements in healthcare, despite existing challenges, and underscores the need for ongoing research to fully realize the potential of AI in this field.
목차
1. Introduction
2. Background
3. Innovative Methodologies
3.1 Disease Prediction using Graph Neural Networks
3.2 Community Detection Techniques in XAI
3.3 Drug Interaction Analysis using Overlapping Network Models
3.4 Patient Health Tracking with Time-Series Network Analysis
4. Case Studies
4.1 XAI-Driven Disease Prediction Models
4.2 Exploring Drug Interactions through Network Analysis
5. Challenges and Future Directions
6. Conclusions
Acknowledgement
References