T1DM personalized care insights through machine and deep learning models: A comprehensive survey

Abstract

Type 1 Diabetes Mellitus (T1DM) is a lifelong condition defined by the immune system's beating on and eradication of the insulin-generating beta cells within the pancreas. The reasoning for this review is that it not only synthesizes an existing finding, but also provides a practical implication for how ML, and DL, can influence thew promising avenues in T1DM personalized care. This will be a helpful roadmap area for researchers, clinicians, and policymakers that are working in the arenas of artificial intelligence, healthcare, and chronic disease management. Precise and timely diagnosis of this debilitating disease, anticipating its progression over the years, as well as customized treatment methodologies are imperative to boosting Quality of Life (QoL) and limiting complications for those with T1DM. Cutting-edge Machine Learning (ML) and Deep Learning (DL) techniques have demonstrated tremendous potential to revolutionize diabetes care delivery through noninvasive, data-driven approaches. By analyzing the immense and diverse real-world data now accessible. These progressive analytical models may provide personalized and actionable insights into disease risk, management strategies, and outcomes for each unique patient. There are several existing researches cited focused towards educational computing as Mydiabetic, Digibete, ITS, and LIDia. The following three trends align within the area of Predictive Modelling, Wearable Devices and Mobile Health Applications, Focus on Early Diagnosis and Personalized Treatment, Emergence of Hybrid Control Policies for Artificial Pancreas Systems, learning strategies for managing diabetes care. The coupling of ML/DL and multimodal data presents exciting critical insights and implications possibilities for personalizing T1DM care. Adopting technologies into everyday clinical practice will require a concentration on long-term validation, interpretability, developments in adaptive interventions, ethics and deployment in User Trust in Intelligent Systems. This comprehensive review discusses and evaluates such promising ML and DL applications, highlighting major accomplishments while also identifying remaining challenges and future research directions for realizing their full benefit in optimizing T1DM management.