Identifying type 2 diabetes mellitus can be challenging, particularly for primary care physicians. Clinical decision support systems incorporating artificial intelligence (AI-CDSS) can assist medical professionals in diagnosing type 2 diabetes with high accuracy. This study aimed to assess an AI-CDSS specifically developed for the diagnosis of type 2 diabetes by employing a hybrid approach that integrates expert-driven insights with machine learning techniques. The AI-CDSS was developed (training dataset: n = 650) and tested (test dataset: n = 648) using a dataset of 1298 patients with and without type 2 diabetes. To generate predictions, the algorithm utilized key features such as body mass index, plasma fasting glucose, and hemoglobin A1C. Furthermore, a clinical pilot study involving 105 patients was conducted to assess the diagnostic accuracy of the system in comparison to non-endocrinology specialists. The AI-CDSS showed a high degree of accuracy, with 99.8% accuracy in predicting diabetes, 99.3% in predicting prediabetes, 99.2% in identifying at-risk individuals, and 98.8% in predicting no diabetes. The test dataset revealed a 98.8% agreement between endocrinology specialists and the AI-CDSS. Type 2 diabetes was identified in 45% of 105 individuals in the pilot study. Compared with diabetes specialists, the AI-CDSS scored a 98.5% concordance rate, greatly exceeding that of nonendocrinology specialists, who had an 85% agreement rate. These findings indicate that the AI-CDSS has the potential to be a useful tool for accurately identifying type 2 diabetes, especially in situations in which diabetes specialists are not readily available.
Diabetica: Adapting Large Language Model to Enhance Multiple Medical Tasks in Diabetes Care and Management
Qing Yuan Research Institute, SEIEE, Shanghai Jiao Tong University（上海交通大学电子信息与电气工程学院清源研究院）；Zhongshan Hospital, Fudan University（复旦大学附属中山医院）；Qingpu Branch of Zhongshan Hospital, Affiliated to Fudan University（复旦大学附属中山医院青浦分院）
DiabetesNet: A Deep Learning Approach to Diabetes Diagnosis
Australian National University（澳大利亚国立大学）；Commonwealth Scientific and Industrial Research Organisation（英联邦科学与工业研究组织）；Curtin University（科廷大学）；Obuda University（欧布达大学）
An empirical evaluation of the risks of AI model updates using clinical data: stability, arbitrariness, and fairness
Spanish Ministry of Science and Innovation（西班牙科学与创新部）；Department of Research and Universities of the Government of Catalonia（加泰罗尼亚政府研究与大学部门）；MCIN/AEI /10.13039/501100011033（MCIN/AEI）；Maria de Maeztu Units of Excellence Programme（玛丽亚·德·玛埃斯特乌卓越计划）
GLYFE: Review and Benchmark of Personalized Glucose Predictive Models in Type-1 Diabetes
CNRS-LIMSI（法国国家科学研究中心-LIMSI）；Université Paris-Saclay（巴黎萨克雷大学）；Université Paris 8（巴黎第八大学）；Samovar；CNRS（法国国家科学研究中心）；Télécom SudParis（南巴黎电信学院）；Institut Polytechnique de Paris（巴黎综合理工学院）
College of Health Solutions, Arizona State University（亚利桑那州立大学健康解决方案学院）；School of Computing and Augmented Intelligence, Arizona State University（亚利桑那州立大学计算与增强智能学院）；Department of Endocrinology, Mayo Clinic Arizona（梅奥诊所亚利桑那分部内分泌科）
Improving Early Prediction of Type 2 Diabetes Mellitus with ECG-DiaNet: A Multimodal Neural Network Leveraging Electrocardiogram and Clinical Risk Factors
College of Science and Engineering, Hamad Bin Khalifa University（哈马德·本·哈利法大学科学与工程学院）
Nurse-in-the-Loop Artificial Intelligence for Precision Management of Type 2 Diabetes in a Clinical Trial Utilizing Transfer-Learned Predictive Digital Twin
Sam Houston State University（山姆休斯顿州立大学）；The University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）；The University of Texas Health San Antonio（德克萨斯大学圣安东尼奥健康科学中心）；Florida State University（佛罗里达州立大学）
Medical Diagnosis with Large Scale Multimodal Transformers: Leveraging Diverse Data for More Accurate Diagnosis
University Hospital Aachen（亚琛大学医院）；RWTH Aachen University（亚琛工业大学）；Ocumeda GmbH（Ocumeda 有限公司）；RWTH Aachen（亚琛工业大学）；Charité – Universitätsmedizin Berlin（柏林夏里特医学院）；Freie Universität Berlin（柏林自由大学）；Technical University Dresden（德累斯顿工业大学）；University of Leeds（利兹大学）；University Hospital Heidelberg（海德堡大学医院）
Clinically Interpretable Mortality Prediction for ICU Patients with Diabetes and Atrial Fibrillation: A Machine Learning Approach
organization= Daniel J. Epstein Department of Industrial \& Systems Engineering, University of Southern California , city= Los Angeles , state= CA , postcode= 90089 , country= United States；organization= Colorectal Research Center, Iran University of Medical Sciences , city= Tehran , postcode= 14535 , country= Iran；organization= Department of Health Science, California State University , city= Long Beach , state= CA , postcode= 90840 , country= United States；organization= Department of Industrial；Manufacturing Engineering, California State Polytechnic University , city= Pomona , state= CA , postcode= 91768 , country= United States
Medical Multimodal Foundation Models in Clinical Diagnosis and Treatment: Applications, Challenges, and Future Directions
School of Biomedical Engineering, Tsinghua University（清华大学生物医学工程学院）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；Hepato-Pancreato-Biliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University（清华大学临床医学院北京清华长庚医院肝胆胰中心）；School of Computer and Communication Engineering, University of Science and Technology Beijing（北京科技大学计算机与通信工程学院）；College of Electronic Information Engineering, Tongji University（同济大学电子与信息工程学院）
Trajectories, bifurcations and pseudotime in large clinical datasets: applications to myocardial infarction and diabetes data
Krasnoyarsk State Medical University（克拉斯诺亚尔斯克国立医科大学）；Institut Curie（居里研究所）；PSL Research University（巴黎文理研究大学）；INSERM（法国国家健康与医学研究院）；CBIO-Centre for Computational Biology（计算生物学中心）；Mines ParisTech（巴黎高等矿业学院）；University of Leicester（莱斯特大学）；Lobachevsky University（罗巴切夫斯基大学）
A Narrative Review of Clinical Decision Support Systems in Offloading Footwear for Diabetes-Related Foot Ulcers
School of Computing, Mathematics and Engineering（计算、数学与工程学院）；Charles Sturt University（查尔斯·斯特劳特大学）；School of Allied Health, Exercise and Sports Sciences（Allied Health, Exercise and Sports Sciences学院）；Foot Balance Technology Pty Ltd
ECO-AMLP: A Decision Support System using an Enhanced Class Outlier with Automatic Multilayer Perceptron for Diabetes Prediction
National University of Sciences & Technology（国家科学与技术大学）；Manchester Metropolitan University（曼彻斯特城市大学）
随着先进的数据分析技术，为疾病预测提供更准确的决策支持系统的努力正在增加。世界卫生组织（WHO）的调查表明，糖尿病患者和相关死亡人数每年都在大幅增加。糖尿病的早期诊断是研究人员和从业人员的主要关注点。本文提出了一个应用\textit{Automatic Multilayer Perceptron }，\textit{ }结合离群检测方法\textit{Enhanced Class Outlier Detection using distance based algorithm }，创建一个预测框架命名为增强类离群自动多层感知器（ECO-AMLP）。一系列的实验进行公开的皮马印度糖尿病数据集比较ECO-AMLP与其他个人分类器以及集成为基础的方法。与其他预处理和分类技术相比，我们框架中使用的离群值技术得到了更好的结果。最后，将结果与文献中报道的其他最先进的方法进行比较，预测PIDD糖尿病的准确性为88.7%，优于所有其他报道的研究。
A novel solution of deep learning for enhanced support vector machine for predicting the onset of type 2 diabetes
Charles Sturt University（查尔斯特大学）；Al Iraqia University（伊拉克大学）；Western Sydney University（西悉尼大学）；Kent Institute Australia（澳大利亚肯特学院）；Asia Pacific International College（亚太国际学院）；University of Kurdistan Hewler（库尔德斯坦休勒大学）；University of New South Wales（新南威尔士大学）
A novel method for Causal Structure Discovery from EHR data, a demonstration on type-2 diabetes mellitus
Institute for Health Informatics, University of Minnesota（明尼苏达大学健康信息学研究所）；Mayo Clinic（梅奥诊所）
导言：发现疾病的因果机制有助于更好地诊断、预后和治疗选择。临床试验一直是确定因果关系的黄金标准，但它们是资源密集型的，有时不可行或不道德。电子健康记录（EHR）包含了丰富的真实世界的数据，有希望发现疾病的机制，但现有的因果结构发现（CSD）的方法不足以利用它们，由于EHR数据的特殊性。我们提出了一种新的数据转换方法和一种新的CSD算法，以克服这些特点所带来的挑战。材料和方法：我们证明了所提出的方法上的应用，2型糖尿病。我们使用Mayo Clinic的大型EHR数据集来内部评估拟议的转换和CSD方法，并使用来自独立卫生系统Fairview Health Services的另一个大型数据集作为外部验证。我们比较了我们提出的方法的性能快速贪婪等价搜索（FGES），一个国家的最先进的CSD方法的正确性，稳定性和完整性。我们通过外部验证测试了所提出的算法的通用性。结果和结论：所提出的方法通过成功地结合研究设计考虑来改进现有方法，在面对不可靠的EHR时间戳时具有鲁棒性，并且更正确可靠地推断因果效应方向。所提出的数据转换成功地提高了所发现的图形的临床正确性和跨引导样本的边缘方向的一致性。它导致了优越的准确性，稳定性和完整性。
Fusing Medical Image Features and Clinical Features with Deep Learning for Computer-Aided Diagnosis
Beijing Institute of Technology（北京理工大学）；Xuanwu Hospital, Capital Medical University（首都医科大学宣武医院）；National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院肿瘤医院（国家癌症中心/国家肿瘤临床医学研究中心））
Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis
Georgia Institute of Technology（佐治亚理工学院）；McGill University（麦吉尔大学）；Carnegie Mellon University（卡内基梅隆大学）；Oregon State University（俄勒冈州立大学）；University of Pennsylvania（宾夕法尼亚大学）；Northeastern University（东北大学）；University of California, San Diego（加州大学圣地亚哥分校）
CopulaSMOTE: A Copula-Based Oversampling Approach for Imbalanced Classification in Diabetes Prediction
Stanford University School of Medicine（斯坦福大学医学院）；Minnesota State University（明尼苏达州立大学）；University of Louisiana at Lafayette（路易斯安那大学拉斐特分校）；Southern Utah University（犹他州南方大学）
Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network
Tsinghua University（清华大学）；Tsinghua National Laboratory of Information Science and Technology（清华信息科学与技术国家实验室）；Beijing University of Posts and Telecommunications（北京邮电大学）；Huangshi Central Hospital of Edong Healthcare Group, Hubei Polytechnic University（湖北理工学院鄂东医疗集团黄石市中心医院）
