About this Research Topic
Machine learning, a potent technique for extracting knowledge from big data, has found broad applications in medical research, including the identification of pathogenic mechanisms, therapeutic targets, prognostic markers, and more. In endocrine disease research, innovative and robust machine-learning algorithms have been developed and applied with considerable success. As the complexity of problems related to digestive diseases continues to increase, there is an increasing need to combine traditional and advanced machine learning techniques for comprehensive analysis.
This study aims to investigate the role of machine learning in the diagnosis, classification, prognosis, prevention, and treatment of digestive system diseases. We welcome original research and reviews focusing on digestive system diseases. Topics of interest may include, but are not limited to:
• Novel models for risk stratification of digestive tract diseases;
• Novel therapeutic strategies for digestive tract diseases guided by machine learning;
• Innovative machine learning algorithms applied to diagnosis and risk prediction of digestive tract diseases;
• Construction of biological databases for digestive tract diseases;
• Exploring vital factors that intervene in disease progression by machine learning;
• Prediction of response to drug and immunotherapy.
Keywords: Machine Learning;Big data;Digestive system diseases
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.