The purpose of this Conference is to present the main lines of base projects that are founded on research already begun in previous years. In this sense, this manuscript will present the main lines of research in Diabetes Mellitus type 1 and Machine Learning techniques in an Internet of Things environment, so that we can summarize the future lines to be developed as follows: data collection through biosensors, massive data processing in the cloud, interconnection of biodevices, local computing vs. cloud computing, and possibilities of machine learning techniques to predict blood glucose values, including both variable selection algorithms and predictive techniques.
Secure and Privacy-Preserving Automated Machine Learning Operations into End-to-End Integrated IoT-Edge-Artificial Intelligence-Blockchain Monitoring System for Diabetes Mellitus Prediction
The University of Melbourne（墨尔本大学）；United Arab Emirates University（阿联酋大学）；Mediclinic（Mediclinic医疗集团）；Bhubaneswar Institute of Technology（布巴内斯瓦尔理工学院）；SRM Institute of Science & Technology（SRM科学技术学院）
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）
Chittagong University of Engineering & Technology（吉大港工程技术大学）
Diabetes mellitus is a common disease of human body caused by a group of metabolic disorders where the sugar levels over a prolonged period is very high. It affects different organs of the human body which thus harm a large number of the body's system, in particular the blood veins and nerves. Early prediction in such disease can be controlled and save human life. To achieve the goal, this research work mainly explores various risk factors related to this disease using machine learning techniques. Machine learning techniques provide efficient result to extract knowledge by constructing predict…
National Technical University of Athens（雅典国立技术大学）；University of Athens（雅典大学）
Cardiovascular Disease (CVD) is an important cause of disability and death among individuals with Diabetes Mellitus (DM). International clinical guidelines for the management of Type 2 DM (T2DM) are founded on primary and secondary prevention and favor the evaluation of CVD related risk factors towards appropriate treatment initiation. CVD risk prediction models can provide valuable tools for optimizing the frequency of medical visits and performing timely preventive and therapeutic interventions against CVD events. The integration of explainability modalities in these models can enhance human…
Faculty of Health Sciences, University of Maribor（马里博尔大学健康科学学院）
Machine Learning (ML) models are often complex and difficult to interpret due to their 'black-box' characteristics. Interpretability of a ML model is usually defined as the degree to which a human can understand the cause of decisions reached by a ML model. Interpretability is of extremely high importance in many fields of healthcare due to high levels of risk related to decisions based on ML models. Calibration of the ML model outputs is another issue often overlooked in the application of ML models in practice. This paper represents an early work in examination of prediction model calibratio…
Leveraging health administrative data (HAD) datasets for predicting the risk of chronic diseases including diabetes has gained a lot of attention in the machine learning community recently. In this paper, we use the largest health records datasets of patients in Ontario,Canada. Provided by the Institute of Clinical Evaluative Sciences (ICES), this database is age, gender and ethnicity-diverse. The datasets include demographics, lab measurements,drug benefits, healthcare system interactions, ambulatory and hospitalizations records. We perform one of the first large-scale machine learning studie…
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）
Introduction: The discovery of causal mechanisms underlying diseases enables better diagnosis, prognosis and treatment selection. Clinical trials have been the gold standard for determining causality, but they are resource intensive, sometimes infeasible or unethical. Electronic Health Records (EHR) contain a wealth of real-world data that holds promise for the discovery of disease mechanisms, yet the existing causal structure discovery (CSD) methods fall short on leveraging them due to the special characteristics of the EHR data. We propose a new data transformation method and a novel CSD alg…
Explainable artificial intelligence model predicting the risk of all-cause mortality in patients with type 2 diabetes mellitus
Lobachevsky State University（罗巴切夫斯基国立大学）；Università Politecnica delle Marche（马尔凯理工大学）；IRCCS INRCA（意大利国家养老与老年病研究所）；Mriya Life Institute, National Academy of Active Longevity（国家积极长寿研究院 Mriya Life 研究所）
Institute of Technology, Nirma University（尼尔玛大学理工学院）
Machine Learning and Artificial Intelligence can be widely used to diagnose chronic diseases so that necessary precautionary treatment can be done in critical time. Diabetes Mellitus which is one of the major diseases can be easily diagnosed by several Machine Learning algorithms. Early stage diagnosis is crucial to prevent dangerous consequences. In this paper we have made a comparative analysis of several machine learning algorithms viz. Random Forest, Decision Tree, Artificial Neural Networks, K Nearest Neighbor, Support Vector Machine, and XGBoost along with feature attribution using SHAP …
United Arab Emirates University（阿联酋大学）；The University of Melbourne（墨尔本大学）
Diabetes Mellitus has no permanent cure to date and is one of the leading causes of death globally. The alarming increase in diabetes calls for the need to take precautionary measures to avoid/predict the occurrence of diabetes. This paper proposes HealthEdge, a machine learning-based smart healthcare framework for type 2 diabetes prediction in an integrated IoT-edge-cloud computing system. Numerical experiments and comparative analysis were carried out between the two most used machine learning algorithms in the literature, Random Forest (RF) and Logistic Regression (LR), using two real-life …
Machine Learning Prediction of Cardiovascular Risk in Type 1 Diabetes Mellitus Using Radiomics Features from Multimodal Retinal Images
Universitat Politècnica de Catalunya (UPC)（加泰罗尼亚理工大学）；Institut Clínic d´Oftalmología (ICOF)（眼科临床研究所）；Hospital Clínic de Barcelona（巴塞罗那临床医院）；Intelligent Data Science and Artificial Intelligence (IDEAI-UPC) Research Center（智能数据科学与人工智能研究中心）；August Pi i Sunyer Biomedical Research Institute (IDIBAPS)（奥古斯特·皮伊·苏涅尔生物医学研究所）；School of Medicine, Universitat de Barcelona（巴塞罗那大学医学院）
Diabetes is currently one of the most common, dangerous, and costly diseases in the world that is caused by an increase in blood sugar or a decrease in insulin in the body. Diabetes can have detrimental effects on people's health if diagnosed late. Today, diabetes has become one of the challenges for health and government officials. Prevention is a priority, and taking care of people's health without compromising their comfort is an essential need. In this study, the Ensemble training methodology based on genetic algorithms are used to accurately diagnose and predict the outcomes of diabetes m…
KenSci Inc.（KenSci公司）；University of Washington（华盛顿大学）
Prediction of diabetes and its various complications has been studied in a number of settings, but a comprehensive overview of problem setting for diabetes prediction and care management has not been addressed in the literature. In this document we seek to remedy this omission in literature with an encompassing overview of diabetes complication prediction as well as situating this problem in the context of real world healthcare management. We illustrate various problems encountered in real world clinical scenarios via our own experience with building and deploying such models. In this manuscri…
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）
Institute for Basic Science (IBS)（基础科学研究院）；Seoul National University（首尔大学）；Pohang University of Science and Technology（浦项科技大学）
Machine learning shows remarkable success for recognizing patterns in data. Here we apply the machine learning (ML) for the diagnosis of early stage diabetes, which is known as a challenging task in medicine. Blood glucose levels are tightly regulated by two counter-regulatory hormones, insulin and glucagon, and the failure of the glucose homeostasis leads to the common metabolic disease, diabetes mellitus. It is a chronic disease that has a long latent period the complicates detection of the disease at an early stage. The vast majority of diabetics result from that diminished effectiveness of…
The imperative for early detection of type 2 diabetes mellitus (T2DM) is challenged by its asymptomatic onset and dependence on suboptimal clinical diagnostic tests, contributing to its widespread global prevalence. While research into noninvasive T2DM screening tools has advanced, conventional machine learning approaches remain limited to unimodal inputs due to extensive feature engineering requirements. In contrast, deep learning models can leverage multimodal data for a more holistic understanding of patients' health conditions. However, the potential of chest X-ray (CXR) imaging, one of th…
Explainable Retinal Imaging for Prediction of Multi-Organ Dysfunction in Type 2 Diabetes
Faculty of Computer Science and Artificial Intelligence（计算机科学与人工智能学院）；Frontier Science Computing Center（前沿科学计算中心）；Chinese Academy of Sciences（中国科学院）；Chinese University of Hong Kong（香港中文大学）；Zhuhai People's Hospital（珠海人民医院）；Beijing Institute of Technology（北京理工大学）；Jinan University（暨南大学）；Lampang Inter-Tech College
CureMD Research and Development（CureMD研发部）；University of the Punjab（旁遮普大学）
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% in…
Multi-Modal Machine Learning for Population- and Subject-Specific lncRNA-Type 2 Diabetes Association Analysis
Department of Electronics and Communication Engineering, IIITDM Jabalpur（IIITDM Jabalpur电子与通信工程系）；Department of Electronics and Communication Engineering, Central University of Jammu（Jammu中央大学电子与通信工程系）
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
SmartEdge: Smart Healthcare End-to-End Integrated Edge and Cloud Computing System for Diabetes Prediction Enabled by Ensemble Machine Learning
University of Melbourne（墨尔本大学）；United Arab Emirates University（阿联酋大学）；Emirates Center for Mobility Research（阿联酋交通研究中心）；National Water and Energy Center（国家水资源与能源中心）；UAE University（阿联酋大学）
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）
Jaypee University of Information Technology（贾伊皮信息技术大学）
From the past few years, due to advancements in technologies, the sedentary living style in urban areas is at its peak. This results in individuals getting a victim of obesity at an early age. There are various health impacts of obesity like Diabetes, Heart disease, Blood pressure problems, and many more. Machine learning from the past few years is showing its implications in all expertise like forecasting, healthcare, medical imaging, sentiment analysis, etc. In this work, we aim to provide a framework that uses machine learning algorithms namely, Random Forest, Decision Tree, XGBoost, Extra …
University of The Witwatersrand（威特沃特斯兰德大学）；University of Johannesburg（约翰内斯堡大学）
Lower-and-middle income countries are faced with challenges arising from a lack of data on cause of death (COD), which can limit decisions on population health and disease management. A verbal autopsy(VA) can provide information about a COD in areas without robust death registration systems. A VA consists of structured data, combining numeric and binary features, and unstructured data as part of an open-ended narrative text. This study assesses the performance of various machine learning approaches when analyzing both the structured and unstructured components of the VA report. The algorithms …
Sample Size in Natural Language Processing within Healthcare Research
Institute of Psychiatry, Psychology and Neurosciences, King’s College London（伦敦国王学院精神病学、心理学与神经科学研究所）；South London and Maudsley NHS Foundation Trust（南伦敦和莫兹利 NHS 基金会信托）；Psychological Institute of the University of Zurich（苏黎世大学心理研究所）
Objectives To develop and evaluate machine learning models to detect suspected undiagnosed nonalcoholic steatohepatitis (NASH) patients for diagnostic screening and clinical management. Methods In this retrospective observational noninterventional study using administrative medical claims data from 1,463,089 patients, gradient-boosted decision trees were trained to detect likely NASH patients from an at-risk patient population with a history of obesity, type 2 diabetes mellitus (T2DM), metabolic disorder, or nonalcoholic fatty liver (NAFL). Models were trained to detect likely NASH in all at-r…
