PDF(Pubmed)
Abstract：
The increasing prevalence of Diabetes Mellitus (DM) as a global health concern highlights the paramount importance of accurately predicting its progression. This necessity has propelled the use of deep learning\'s advanced analytical and predictive capabilities to the forefront of current research. However, this approach is confronted with significant challenges, notably the prevalence of incomplete data and the need for more robust predictive models. Our research aims to address these critical issues, leveraging deep learning to enhance the precision and reliability of diabetes progression predictions. We address the issue of missing data by first locating individuals with data gaps within specific patient clusters, and then applying targeted imputation strategies for effective data imputation. To enhance the robustness of our model, we implement strategies such as data augmentation and the development of advanced group-level feature analysis. A cornerstone of our approach is the implementation of a deep attentive transformer that is sensitive to group characteristics. This framework excels in processing a wide array of data, including clinical and physical examination information, to accurately predict the progression of DM. Beyond its predictive capabilities, our model is engineered to perform advanced feature selection and reasoning. This is crucial for understanding the impact of both individual and group-level factors on deep models\' predictions, providing invaluable insights into the dynamics of DM progression. Our approach not only marks a significant advancement in the prediction of diabetes progression but also contributes to a deeper understanding of the multifaceted factors influencing this chronic disease, thereby aiding in more effective diabetes management and research.
摘要：
糖尿病（DM）作为全球健康问题的患病率日益增加，突显了准确预测其进展的重要性。这种必要性推动了深度学习的先进分析和预测能力的使用，以目前的研究前沿。然而,这种方法面临着重大挑战，特别是不完整数据的普遍性和对更稳健的预测模型的需求。我们的研究旨在解决这些关键问题，利用深度学习提高糖尿病进展预测的准确性和可靠性。我们通过首先定位特定患者群中存在数据缺口的个体来解决数据缺失的问题，然后应用有针对性的填补策略进行有效的数据填补。为了增强我们模型的鲁棒性，我们实施了数据增强和开发高级组级特征分析等策略。我们方法的基石是实施对群体特征敏感的深层专注转换器。这个框架擅长处理各种各样的数据，包括临床和体检信息，以准确预测DM的进展。除了它的预测能力，我们的模型被设计来执行高级特征选择和推理。这对于理解个人和群体层面因素对深度模型预测的影响至关重要。为DM进展的动态提供宝贵的见解。我们的方法不仅标志着糖尿病进展预测的重大进展，而且有助于更深入地了解影响这种慢性疾病的多方面因素。从而帮助更有效的糖尿病管理和研究。
