Optimizing Disease Prediction with Artificial Intelligence Driven Feature Selection and Attention Networks
Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology（Vel Tech Rangarajan Dr. Sagunthala 研发科学与技术研究所）；R.M.K. Engineering College（R.M.K.工程学院）；P.S.R. Engineering College（P.S.R.工程学院）
D. Dhinakaran, S. Edwin Raja, M. Thiyagarajan, J. Jeno Jasmine, P. Raghavan
The rapid integration of machine learning methodologies in healthcare has ignited innovative strategies for disease prediction, particularly with the vast repositories of Electronic Health Records (EHR) data. This article delves into the realm of multi-disease prediction, presenting a comprehensive study that introduces a pioneering ensemble feature selection model. This model, designed to optimize learning systems, combines statistical, deep, and optimally selected features through the innovative Stabilized Energy Valley Optimization with Enhanced Bounds (SEV-EB) algorithm. The objective is to achieve unparalleled accuracy and stability in predicting various disorders. This work proposes an advanced ensemble model that synergistically integrates statistical, deep, and optimally selected features. This combination aims to enhance the predictive power of the model by capturing diverse aspects of the health data. At the heart of the proposed model lies the SEV-EB algorithm, a novel approach to optimal feature selection. The algorithm introduces enhanced bounds and stabilization techniques, contributing to the robustness and accuracy of the overall prediction model. To further elevate the predictive capabilities, an HSC-AttentionNet is introduced. This network architecture combines deep temporal convolution capabilities with LSTM, allowing the model to capture both short-term patterns and long-term dependencies in health data. Rigorous evaluations showcase the remarkable performance of the proposed model. Achieving a 95% accuracy and 94% F1-score in predicting various disorders, the model surpasses traditional methods, signifying a significant advancement in disease prediction accuracy. The implications of this research extend beyond the confines of academia.
Symptom checkers have been widely adopted as an intelligent e-healthcare application during the ongoing pandemic crisis. Their performance have been limited by the fine-grained quality of the collected medical knowledge between symptom and diseases. While the binarization of the relationships between symptoms and diseases simplifies the data collection process, it also leads to non-convex optimization problems during the inference step. In this paper, we formulate the symptom checking problem as an underdertermined non-convex optimization problem, thereby justifying the use of the compressive …
Artificial Intelligence for Personalized Prediction of Alzheimer's Disease Progression: A Survey of Methods, Data Challenges, and Future Directions
Department of Industrial Engineering and Management Systems University of Central Florida（工业工程与管理系统系 佛罗里达中央大学）；Institute for Simulation and Training University of Central Florida（模拟与培训研究所 佛罗里达中央大学）；Department of Internal Medicine University of Central Florida（内科医学系 佛罗里达中央大学）；Department of Statistics and Data Science University of Central Florida（统计学与数据科学系 佛罗里达中央大学）
Artificial Intelligence-Enabled Spirometry for Early Detection of Right Heart Failure
Department of Computer Science, Tianjin University of Technology（天津理工大学计算机科学系）；National Institute of Health Data Science, Peking University（北京大学国家健康数据科学研究院）；Department of Cardiology, Peking University People’s Hospital（北京大学人民医院心内科）；DCST, BNRist, RIIT, Institute of Internet Industry, Tsinghua University（清华大学互联网产业研究院）；HeartVoice Medical Technology（心声医疗科技）；State Key Laboratory of Vascular Homeostasis and Remodeling, NHC Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides, Peking University（北京大学血管稳态与重塑国家重点实验室）；Institute of Medical Technology, Peking University Health Science Center（北京大学医学部医学技术研究所）；Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）
Enhancing Diagnostic Accuracy for Urinary Tract Disease through Explainable SHAP-Guided Feature Selection and Classification
Labcin - Nature Inspired Computing Laboratory（Labcin - 自然启发计算实验室）；Federal University of Espírito Santo（巴西圣埃斯皮里图联邦大学）；Graduate Program in Computer Science（计算机科学研究生项目）
ADAPT SFI Research Centre（ADAPT科学基金会爱尔兰研究中心）；University College Dublin（都柏林大学）；Liaquat University of Medical and Health Sciences（利亚卡特医学与健康科学大学）
Heart Disease has become one of the most serious diseases that has a significant impact on human life. It has emerged as one of the leading causes of mortality among the people across the globe during the last decade. In order to prevent patients from further damage, an accurate diagnosis of heart disease on time is an essential factor. Recently we have seen the usage of non-invasive medical procedures, such as artificial intelligence-based techniques in the field of medical. Specially machine learning employs several algorithms and techniques that are widely used and are highly useful in accu…
Artificial Intelligence for Infectious Disease Prediction and Prevention: A Comprehensive Review
École supérieure en Sciences et Technologies de l’Informatique et du Numérique（计算机与数字科学技术高等学院）；University of Paris 8（巴黎第八大学）；Université de Bejaia（贝贾亚大学）
HyCARD-Net: A Synergistic Hybrid Intelligence Framework for Cardiovascular Disease Diagnosis
Faculty of Information Science and Technology（信息科学与技术学院）；American International University–Bangladesh（美国国际大学-孟加拉国）；School of Automotive Engineering（汽车工程学院）；Chengdu Industry and Trade College（成都行业贸易学院）；Faculty of Education（教育学院）；Shinawatra University（辛纳瓦特大学）；Tilburg School of Social and Behavioral Sciences（蒂尔堡社会与行为科学学院）；Tilburg University（蒂尔堡大学）
SimulaMet；Oslo Metropolitan University（奥斯陆城市大学）；Odense University Hospital（欧登塞大学医院）；Ifocus；Oslo University Hospital（奥斯陆大学医院）
Dry eye disease (DED) has a prevalence of between 5 and 50\%, depending on the diagnostic criteria used and population under study. However, it remains one of the most underdiagnosed and undertreated conditions in ophthalmology. Many tests used in the diagnosis of DED rely on an experienced observer for image interpretation, which may be considered subjective and result in variation in diagnosis. Since artificial intelligence (AI) systems are capable of advanced problem solving, use of such techniques could lead to more objective diagnosis. Although the term `AI' is commonly used, recent succe…
A Survey of Artificial Intelligence in Gait-Based Neurodegenerative Disease Diagnosis
Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY), Nanyang Technological University（南洋理工大学）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算机与数据科学学院）
Predictors of disease outbreaks at continentalscale in the African region: Insights and predictions with geospatial artificial intelligence using earth observations and routine disease surveillance data
BrightWorld Labs（光明世界实验室）；WHO Regional Office for Africa（世界卫生组织非洲区域办事处）
Pest and disease control plays a key role in agriculture since the damage caused by these agents are responsible for a huge economic loss every year. Based on this assumption, we create an algorithm capable of detecting rust (Hemileia vastatrix) and leaf miner (Leucoptera coffeella) in coffee leaves (Coffea arabica) and quantify disease severity using a mobile application as a high-level interface for the model inferences. We used different convolutional neural network architectures to create the object detector, besides the OpenCV library, k-means, and three treatments: the RGB and value to q…
Explainable Artificial Intelligence (AI) in the form of an interpretable and semiautomatic approach to stage grading ocular pathologies such as Diabetic retinopathy, Hypertensive retinopathy, and other retinopathies on the backdrop of major systemic diseases. The experimental study aims to evaluate an explainable staged grading process without using deep Convolutional Neural Networks (CNNs) directly. Many current CNN-based deep neural networks used for diagnosing retinal disorders might have appreciable performance but fail to pinpoint the basis driving their decisions. To improve these decisi…
School of Engineering, Lakehead University（湖首大学工程学院）
Alzheimer's Disease (AD) is a severe brain disorder, destroying memories and brain functions. AD causes chronically, progressively, and irreversibly cognitive declination and brain damages. The reliable and effective evaluation of early dementia has become essential research with medical imaging technologies and computer-aided algorithms. This trend has moved to modern Artificial Intelligence (AI) technologies motivated by deeplearning success in image classification and natural language processing. The purpose of this review is to provide an overview of the latest research involving deep-lear…
Advancing Digital Precision Medicine for Chronic Fatigue Syndrome through Longitudinal Large-Scale Multi-Modal Biological Omics Modeling with Machine Learning and Artificial Intelligence
AI-Driven Non-Invasive Detection and Staging of Steatosis in Fatty Liver Disease Using a Novel Cascade Model and Information Fusion Techniques
University of Tehran（德黑兰大学）；York University（约克大学）；Northeastern University（东北大学）；Antalya Bilim University（安塔利亚比尔姆大学）；Research Institute for Gastroenterology and Liver Diseases（胃肠病学和肝病学研究所）；Shahid Beheshti University of Medical Sciences（沙希德贝赫什提医科大学）；University of Waterloo（滑铁卢大学）
Pandit Deendayal Petroleum University（潘迪特·迪恩达亚尔石油大学）；Confirm SFI Centre for Smart Manufacturing（Confirm SFI智能制造中心）；Data Science Institute（数据科学研究所）；National University of Ireland Galway（爱尔兰国立大学戈尔韦分校）
With the increasing availability of structured and unstructured data and the swift progress of analytical techniques, Artificial Intelligence (AI) is bringing a revolution to the healthcare industry. With the increasingly indispensable role of AI in healthcare, there are growing concerns over the lack of transparency and explainability in addition to potential bias encountered by predictions of the model. This is where Explainable Artificial Intelligence (XAI) comes into the picture. XAI increases the trust placed in an AI system by medical practitioners as well as AI researchers, and thus, ev…
Helmholtz Zentrum München（慕尼黑亥姆霍兹中心）；Technical University München（慕尼黑工业大学）
Machine learning (ML), deep learning (DL), and artificial intelligence (AI) are of increasing importance in biomedicine. The goal of this work is to show progress in ML in digital health, to exemplify future needs and trends, and to identify any essential prerequisites of AI and ML for precision health. High-throughput technologies are delivering growing volumes of biomedical data, such as large-scale genome-wide sequencing assays, libraries of medical images, or drug perturbation screens of healthy, developing, and diseased tissue. Multi-omics data in biomedicine is deep and complex, offering…
End-to-end (E2E) artificial intelligence (AI) pipelines are composed of several stages including data preprocessing, data ingestion, defining and training the model, hyperparameter optimization, deployment, inference, postprocessing, followed by downstream analyses. To obtain efficient E2E workflow, it is required to optimize almost all the stages of pipeline. Intel Xeon processors come with large memory capacities, bundled with AI acceleration (e.g., Intel Deep Learning Boost), well suited to run multiple instances of training and inference pipelines in parallel and has low total cost of owne…
Cancer is one of the most dangerous diseases to humans, and yet no permanent cure has been developed for it. Breast cancer is one of the most common cancer types. According to the National Breast Cancer foundation, in 2020 alone, more than 276,000 new cases of invasive breast cancer and more than 48,000 non-invasive cases were diagnosed in the US. To put these figures in perspective, 64% of these cases are diagnosed early in the disease's cycle, giving patients a 99% chance of survival. Artificial intelligence and machine learning have been used effectively in detection and treatment of severa…
University of Tennessee Health Science Center（田纳西大学健康科学中心）；University of Tennessee（田纳西大学）；Ochsner Clinic Foundation（奥克斯纳诊所基金会）
This study sought to (1) expand our existing Urban Population Health Observatory (UPHO) system by incorporating a semantics layer; (2) cohesively employ machine learning and semantic/logical inference to provide measurable evidence and detect pathways leading to undesirable health outcomes; (3) provide clinical use case scenarios and design case studies to identify socioenvironmental determinants of health associated with the prevalence of obesity, and (4) design a dashboard that demonstrates the use of UPHO in the context of obesity surveillance using the provided scenarios. The system design…
Pioneer Alpha（先锋阿尔法）；University of Central Florida（中佛罗里达大学）；Ahsanullah University of Science and Technology（阿赫桑努拉科技大学）
In December 2019, a novel virus called COVID-19 had caused an enormous number of causalities to date. The battle with the novel Coronavirus is baffling and horrifying after the Spanish Flu 2019. While the front-line doctors and medical researchers have made significant progress in controlling the spread of the highly contiguous virus, technology has also proved its significance in the battle. Moreover, Artificial Intelligence has been adopted in many medical applications to diagnose many diseases, even baffling experienced doctors. Therefore, this survey paper explores the methodologies propos…
In this study, a novel application of neural networks that predict thermal comfort states of occupants is proposed with accuracy over 95%, and two optimization algorithms are proposed and evaluated under two real cases (general offices and lecture theatres/conference rooms scenarios) in Singapore. The two optimization algorithms are Bayesian Gaussian process optimization (BGPO) and augmented firefly algorithm (AFA). Based on our earlier studies, the models of energy consumption were developed and well-trained through neural networks. This study focuses on using novel active approaches to evalu…
University of Birjand（比尔詹德大学）；Deakin University（迪肯大学）；Iran University of Medical Sciences（伊朗医科大学）；Omid hospital（奥米德医院）；Maragheh Faculty of Medical Sciences（马拉盖医学院）；Universiti Kebangsaan Malaysia（马来西亚国民大学）
Cardiovascular disease is one of the most challenging diseases in middle-aged and older people, which causes high mortality. Coronary artery disease (CAD) is known as a common cardiovascular disease. A standard clinical tool for diagnosing CAD is angiography. The main challenges are dangerous side effects and high angiography costs. Today, the development of artificial intelligence-based methods is a valuable achievement for diagnosing disease. Hence, in this paper, artificial intelligence methods such as neural network (NN), deep neural network (DNN), and Fuzzy C-Means clustering combined wit…
Purpose: To create and evaluate the accuracy of an artificial intelligence Deep learning platform (ORAiCLE) capable of using only retinal fundus images to predict both an individuals overall 5 year cardiovascular risk (CVD) and the relative contribution of the component risk factors that comprise this risk. Methods: We used 165,907 retinal images from a database of 47,236 patient visits. Initially, each image was paired with biometric data age, ethnicity, sex, presence and duration of diabetes a HDL/LDL ratios as well as any CVD event wtihin 5 years of the retinal image acquisition. A risk sco…
Riphah International University（里法国际大学）；Prince Sultan University（苏丹王子大学）；Liverpool John Moores University（利物浦约翰摩尔大学）；Staffordshire University（斯塔福德郡大学）；Prince Sattam bin Abdulaziz University（萨塔姆·本·阿卜杜勒阿齐兹王子大学）
Central Serous Retinopathy (CSR) or Central Serous Chorioretinopathy (CSC) is a significant disease that causes blindness and vision loss among millions of people worldwide. It transpires as a result of accumulation of watery fluids behind the retina. Therefore, detection of CSR at early stages allows preventive measures to avert any impairment to the human eye. Traditionally, several manual methods for detecting CSR have been developed in the past; however, they have shown to be imprecise and unreliable. Consequently, Artificial Intelligence (AI) services in the medical field, including autom…
With the advancements in computer technology, there is a rapid development of intelligent systems to understand the complex relationships in data to make predictions and classifications. Artificail Intelligence based framework is rapidly revolutionizing the healthcare industry. These intelligent systems are built with machine learning and deep learning based robust models for early diagnosis of diseases and demonstrates a promising supplementary diagnostic method for frontline clinical doctors and surgeons. Machine Learning and Deep Learning based systems can streamline and simplify the steps …
Automatic Diagnosis of Myocarditis Disease in Cardiac MRI Modality using Deep Transformers and Explainable Artificial Intelligence
The Graduate School of Biomedical Engineering, UNSW Sydney（新南威尔士大学悉尼分校生物医学工程研究生院）；Data Science and Computational Intelligence Institute, University of Granada（格拉纳达大学数据科学与计算智能研究所）；University of La Rioja（拉里奥哈大学）；University of Technology Sydney (UTS)（悉尼科技大学）；Macquarie University（麦考瑞大学）；University of Leicester（莱斯特大学）；Deakin University（迪肯大学）；University of Cambridge（剑桥大学）；University of Southern Queensland（南昆士兰大学）；The University of New South Wales（新南威尔士大学）；AI-enabled Processes (AIP) Research Centre, Macquarie University（麦考瑞大学人工智能赋能流程研究中心）
Drug repositioning holds great promise because it can reduce the time and cost of new drug development. While drug repositioning can omit various R&D processes, confirming pharmacological effects on biomolecules is essential for application to new diseases. Biomedical explainability in a drug repositioning model can support appropriate insights in subsequent in-depth studies. However, the validity of the XAI methodology is still under debate, and the effectiveness of XAI in drug repositioning prediction applications remains unclear. In this study, we propose GraphIX, an explainable drug reposi…
Artificial intelligence (AI) is supposed to help us make better choices. Some of these choices are small, like what route to take to work, or what music to listen to. Others are big, like what treatment to administer for a disease or how long to sentence someone for a crime. If AI can assist with these big decisions, we might think it can also help with hard choices, cases where alternatives are neither better, worse nor equal but on a par. The aim of this paper, however, is to show that this view is mistaken: the fact of parity shows that there are hard limits on AI in decision making and cho…
The issue in respiratory sound classification has attained good attention from the clinical scientists and medical researcher's group in the last year to diagnosing COVID-19 disease. To date, various models of Artificial Intelligence (AI) entered into the real-world to detect the COVID-19 disease from human-generated sounds such as voice/speech, cough, and breath. The Convolutional Neural Network (CNN) model is implemented for solving a lot of real-world problems on machines based on Artificial Intelligence (AI). In this context, one dimension (1D) CNN is suggested and implemented to diagnose …
Sharif University of Technology（谢里夫理工大学）；Tehran University of Medical Sciences（德黑兰医科大学）；Iran University of Medical Sciences（伊朗医科大学）
COVID-19 is a virus with high transmission rate that demands rapid identification of the infected patients to reduce the spread of the disease. The current gold-standard test, Reverse-Transcription Polymerase Chain Reaction (RT-PCR), has a high rate of false negatives. Diagnosing from CT-scan images as a more accurate alternative has the challenge of distinguishing COVID-19 from other pneumonia diseases. Artificial intelligence can help radiologists and physicians to accelerate the process of diagnosis, increase its accuracy, and measure the severity of the disease. We designed a new interpret…
Aristotle University of Thessaloniki（塞萨洛尼基亚里士多德大学）；AHEPA University Hospital（阿希帕大学医院）
Arrhythmogenic right ventricular cardiomyopathy (ARVC) is an inherited heart muscle disease that appears between the second and forth decade of a patient's life, being responsible for 20% of sudden cardiac deaths before the age of 35. The effective and punctual diagnosis of this disease based on Electrocardiograms (ECGs) could have a vital role in reducing premature cardiovascular mortality. In our analysis, we firstly outline the digitalization process of paper - based ECG signals enhanced by a spatial filter aiming to eliminate dark regions in the dataset's images that do not correspond to E…
Objective: To combine medical knowledge and medical data to interpretably predict the risk of disease. Methods: We formulated the disease prediction task as a random walk along a knowledge graph (KG). Specifically, we build a KG to record relationships between diseases and risk factors according to validated medical knowledge. Then, a mathematical object walks along the KG. It starts walking at a patient entity, which connects the KG based on the patient current diseases or risk factors and stops at a disease entity, which represents the predicted disease. The trajectory generated by the objec…
Arison School of Business, Interdisciplinary Center（阿里森商学院，跨学科中心）
A key step in medical diagnosis is giving the patient a universally recognized label (e.g. Appendicitis) which essentially assigns the patient to a class(es) of patients with similar body failures. However, two patients having the same disease label(s) with high probability may still have differences in their feature manifestation patterns implying differences in the required treatments. Additionally, in many cases, the labels of the primary diagnoses leave some findings unexplained. Medical diagnosis is only partially about probability calculations for label X or Y. Diagnosis is not complete …
The Hong Kong University of Science and Technology（香港科技大学）
Artificial intelligence has provided us with an exploration of a whole new research era. As more data and better computational power become available, the approach is being implemented in various fields. The demand for it in health informatics is also increasing, and we can expect to see the potential benefits of its applications in healthcare. It can help clinicians diagnose disease, identify drug effects for each patient, understand the relationship between genotypes and phenotypes, explore new phenotypes or treatment recommendations, and predict infectious disease outbreaks with high accura…
University College London（伦敦大学学院）；Bioxydyn Limited（博克西丁有限公司）
The COVID-19 pandemic, caused by the severe acute respiratory syndrome coronavirus 2, emerged into a world being rapidly transformed by artificial intelligence (AI) based on big data, computational power and neural networks. The gaze of these networks has in recent years turned increasingly towards applications in healthcare. It was perhaps inevitable that COVID-19, a global disease propagating health and economic devastation, should capture the attention and resources of the world's computer scientists in academia and industry. The potential for AI to support the response to the pandemic has …
Optimizing Convolutional Neural Networks for Chronic Obstructive Pulmonary Disease Detection in Clinical Computed Tomography Imaging
Technical University of Munich（慕尼黑工业大学）；Munich Institute of Biomedical Engineering（慕尼黑生物医学工程研究所）；Klinikum rechts der Isar（伊萨尔河右岸医院）；Institute for Advanced Study, Technical University of Munich（慕尼黑工业大学高等研究院）
