Machine Learning Approaches for Differential Diagnosis, Prognosis, Prevention, and Treatment of Digestive System Disorders

The advent of high-throughput sequencing has ushered in an era of unprecedented data generation, which has been utilized to probe the etiology of diverse human diseases. The challenge posed by the massive data volume is to identify meaningful insights, a task of utmost importance in the medical field.

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.