Tapping the power of multidimensional clinical data to identify Irritable Bowel Syndrome (IBS) endo-phenotype using Machine Learning

Abstract

Irritable bowel syndrome (IBS) physiopathology is a complicated and multidimensional condition that continues to confound researchers and clinicians alike. Despite substan tial research, our understanding of the underlying mechanism remains limited. IBS has long been associated with genetic variables, implying a hereditary component. However, efforts to discover specific genetic risk factors for IBS have been limited and inconclusive. This can be attributable, at least in part, to the complex character of the IBS pheno type, which extends beyond gastrointestinal symptoms alone. The existence of various comorbidities complicates the diagnosis of IBS, as the phenotype cannot be effectively described without taking into account the wide range of concomitant conditions. The use of artificial intelligence methodologies in the examination of irritable bowel syndrome (IBS) has garnered considerable interest in recent years. To understand the intricate complexities associated with this condition, researchers have investigated different tech niques such as predictive modeling, pattern recognition, and data mining. Although machine learning techniques have shown potential, research findings on their implemen tation in the context of IBS are currently restricted. The availability of comprehensive and high-quality data sets poses a significant challenge when conducting research on IBS using machine learning. The multifactorial nature of IBS, with its complex interplay between genetic, environmental, and psychosocial factors, poses significant hurdles for accurately modeling and predicting the disease. The objective of our research was to in vestigate the UK’s largest database, UK BioBank, in order to identify endophenotypes of multidimensional patient data pertaining to Irritable Bowel Syndrome (IBS). These data encompassed various factors such as gastrointestinal symptoms, co-morbidities, demographics, physiology, and psychophysiology. Preliminary inquiries will improve our understanding of this disease and consequently improve the delivery of treatment through tailored medical approaches.