Type 2 diabetes mellitus (T2DM) is one of the most common diseases and a leading cause of death. The problem of early diagnosis of T2DM is challenging and necessary to prevent serious complications. This study proposes a novel neural network architecture for early T2DM prediction using multi-headed self-attention and dense layers to extract features from historic diagnoses, patient vitals, and demographics. The proposed technique is called the Self-Attention for Comorbid Disease Net (SACDNet), achieving an accuracy of 89.3% and an F1-Score of 89.1%, having a 1.6% increased accuracy and 1.3% increased f1-score compared to the baseline techniques. Monte Carlo (MC) Dropout is applied to the SACDNet to get a bayesian approximation. A T2DM prediction framework based on the MC Dropout SACDNet is proposed to quantize the uncertainty associated with the predictions. A T2DM prediction dataset is also built as part of this study which is based on real-world routine Electronic Health Record (EHR) data comprising 4,124 diabetic and 181,767 non-diabetic examples, collected from 295 different EHR systems running in different parts of the United States of America. This dataset is further used to evaluate 7 different machine learning and 3 deep learning-based models. Finally, a detailed analysis of the fairness of every technique against different patient demographic groups is performed to validate the unbiased generalization of the techniques and the diversity of the data.

翻译：2型糖尿病(T2DM)是最常见的疾病之一,也是导致死亡的主要原因之一。T2DM早期诊断问题具有挑战性,是预防严重并发症所必须的。本研究报告提出一个新的神经网络结构,用于利用多头自我关注和密集层对T2DM进行早期预测,以提取历史诊断、病人生命力和人口结构的特征。拟议的技术称为“Comorbid疾病自控网(SACDNet)”,精确度达到89.3%,F1-Score为89.1%,与基线技术相比,病人的准确度提高了1.6%,F1-Score增加了1.3%。Mote Carlo(MC)对SACDNet应用了一种新型T2DM的网络结构,以便利用多头自我关注和密集的层进行近距离近距离测。基于MSdropout SACDNet(SACDNet) 的T2DM预测框架旨在消除与预测有关的不确定性。T2DM预测数据集也是作为这项研究的一部分,这项研究基于真实世界常规电子健康记录(EH)的数据,包括4,糖尿病和181,7,7,7-7-67(ML)的公平性分析方法的不断学习美国不同数据,这是从不同系统、不同数据系统进行的、不同系统、不同数据学、不断学习的每个系统、不同数据分析,这是对不同数据系统进行的第3-295、第3段的、第3段进行不同的、第3段的、第3段的、第3段进行进一步评估。