SACDNet: Towards Early Type 2 Diabetes Prediction with Uncertainty for Electronic Health Records
CureMD Research and Development（CureMD研发部）；University of the Punjab（旁遮普大学）
Tayyab Nasir, Muhammad Kamran Malik
备注：
Misspelled SACEDNet changed to SACDNet in Abstract. Related Work corrected tehcniques to techniques. Dataset rh replaced with Rh. Methodology, mod replaced with models, andrepresentation with representations. Removed bold formatting from Table 3 in Methodology. Results replaced Hence the with Hence, this
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.
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）
First Experiences with the Identification of People at Risk for Diabetes in Argentina using Machine Learning Techniques
Facultad de Informática, UNLP（拉普拉塔大学信息学院）；Comisión de Investigaciones Científicas (CIC)（科学研究委员会）；CENEXA, Facultad de C. Médicas, UNLP-CONICET（拉普拉塔大学-国家科学技术研究委员会医学系CENEXA中心）；CONICET（国家科学技术研究委员会）
SweetDeep: A Wearable AI Solution for Real-Time Non-Invasive Diabetes Screening
Department of Electrical and Computer Engineering, Princeton University（普林斯顿大学电气与计算机工程系）；NeuTigers, Inc.（NeuTigers公司）；AP-HP Avicennes, Bobigny, France（法国AP-HP阿维尼翁医院）；Clinic International Constantine, Constantine, Algeria（阿尔及利亚康斯坦丁国际诊所）
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。
CardioMeta: Calibrated Multi-Task Prediction of Diabetes, Hypertension, and Cardiovascular Disease Across Population and EHR Data
School of Computing, Wichita State University（威奇托州立大学计算学院）；Department of Computer Science and Engineering, American International University-Bangladesh（孟加拉国美国国际大学计算机科学与工程系）；School of Computer Science and Engineering, The University of Aizu（会津大学计算机科学与工程学院）
Artificial Intelligence-Based Methods for Precision Medicine: Diabetes Risk Prediction
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）；Weill Cornell Medicine-Qatar（威尔康奈尔医学院卡塔尔分校）；College of Health and Life Sciences, Hamad Bin Khalifa University（哈马德·本·哈利法大学健康与生命科学学院）
Context-Aware Optimization of Follow-Up Intervals for Type 2 Diabetes Care Using Markov Decision Processes
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；National Center for Human Factors in Healthcare（医疗人因国家中心）；Georgetown University School of Medicine（乔治城大学医学院）；Medstar Georgetown University Hospital（Medstar乔治城大学医院）
Directive Explanations for Monitoring the Risk of Diabetes Onset: Introducing Directive Data-Centric Explanations and Combinations to Support What-If Explorations
Developing A Fair Individualized Polysocial Risk Score (iPsRS) for Identifying Increased Social Risk of Hospitalizations in Patients with Type 2 Diabetes (T2D)
University of Florida（佛罗里达大学）；University of Florida College of Medicine（佛罗里达大学医学院）
A Structural FHMM for Interpretable Disease Trajectories in T2DM
Swiss Data Science Center (SDSC)（瑞士数据科学中心（SDSC））；Ecole Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）；ETH Zürich（苏黎世联邦理工学院）；Institute of Pharmaceutical Sciences（药学院）
Deep Representation Learning of Electronic Health Records to Unlock Patient Stratification at Scale
Bruno Kessler Institute（布鲁诺·凯斯勒研究所）；University of Trento（特伦托大学）；Hasso Plattner Institute for Digital Health at Mount Sinai（西奈山哈索·普拉特纳数字健康研究所）；Institute for Next Generation Healthcare（下一代医疗保健研究所）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；Azienda USL Centro “Santi”（圣蒂中央地方卫生局）；HK3 Lab（HK3实验室）
