Download or read book Machine Learning in Disease Screening Diagnosis and Surveillance written by Yi-Ju Tseng and published by Frontiers Media SA. This book was released on 2023-04-11 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book AI for Disease Surveillance and Pandemic Intelligence written by Arash Shaban-Nejad and published by Springer Nature. This book was released on 2022-03-08 with total page 335 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book aims to highlight the latest achievements in the use of artificial intelligence for digital disease surveillance, pandemic intelligence, as well as public and clinical health surveillance. The edited book contains selected papers presented at the 2021 Health Intelligence workshop, co-located with the Association for the Advancement of Artificial Intelligence (AAAI) annual conference, and presents an overview of the issues, challenges, and potentials in the field, along with new research results. While disease surveillance has always been a crucial process, the recent global health crisis caused by COVID-19 has once again highlighted our dependence on intelligent surveillance infrastructures that provide support for making sound and timely decisions. This book provides information for researchers, students, industry professionals, and public health agencies interested in the applications of AI in population health and personalized medicine.

Download or read book Machine Learning and Data Analytics for Predicting Managing and Monitoring Disease written by Roy, Manikant and published by IGI Global. This book was released on 2021-06-25 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data analytics is proving to be an ally for epidemiologists as they join forces with data scientists to address the scale of crises. Analytics examined from many sources can derive insights and be used to study and fight global outbreaks. Pandemic analytics is a modern way to combat a problem as old as humanity itself: the proliferation of disease. Machine Learning and Data Analytics for Predicting, Managing, and Monitoring Disease explores different types of data and discusses how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more by applying cutting edge technology such as machine learning and data analytics in the wake of the COVID-19 pandemic. Covering a range of topics such as mental health analytics during COVID-19, data analysis and machine learning using Python, and statistical model development and deployment, it is ideal for researchers, academicians, data scientists, technologists, data analysts, diagnosticians, healthcare professionals, computer scientists, and students.

Download or read book Diagnostic Applications of Health Intelligence and Surveillance Systems written by Yadav, Divakar and published by IGI Global. This book was released on 2021-01-15 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: Health surveillance and intelligence play an important role in modern health systems as more data must be collected and analyzed. It is crucial that this data is interpreted and analyzed effectively and efficiently in order to assist with diagnoses and predictions. Diagnostic Applications of Health Intelligence and Surveillance Systems is an essential reference book that examines recent studies that are driving artificial intelligence in the health sector and helping practitioners to predict and diagnose diseases. Chapters within the book focus on health intelligence and how health surveillance data can be made useful and meaningful. Covering topics that include computational intelligence, data analytics, mobile health, and neural networks, this book is crucial for healthcare practitioners, IT specialists, academicians, researchers, and students.

Download or read book Surveillance Prevention and Control of Infectious Diseases written by Muhammad E. H. Chowdhury and published by Springer Nature. This book was released on with total page 247 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Disease Prediction using Machine Learning Deep Learning and Data Analytics written by Geeta Rani, Vijaypal Singh Dhaka, Pradeep Kumar Tiwari and published by Bentham Science Publishers. This book was released on 2024-03-07 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a comprehensive review of technologies and data in healthcare services. It features a compilation of 10 chapters that inform readers about the recent research and developments in this field. Each chapter focuses on a specific aspect of healthcare services, highlighting the potential impact of technology on enhancing practices and outcomes. The main features of the book include 1) referenced contributions from healthcare and data analytics experts, 2) a broad range of topics that cover healthcare services, and 3) demonstration of deep learning techniques for specific diseases. Key topics: - Federated learning in analysis of sensitive healthcare data while preserving privacy and security. - Artificial intelligence for 3-D bone image reconstruction. - Detection of disease severity and creating personalized treatment plans using machine learning and software tools - Case studies for disease detection methods for different disease and conditions, including dementia, asthma, eye diseases - Brain-computer interfaces - Data mining for standardized electronic health records - Data collection, management, and analysis in epidemiological research The book is a resource for learners and professionals in healthcare service training programs and health administration departments. Readership Learners and professionals in healthcare service training programs and health administration departments.

Download or read book Chronic Disease Status Identification from De identified Clinical Records Based on Machine Learning written by Kunal Rajput and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Automatic detection and identification of chronic disease conditions from de-identified clinical records can give timely support to the medical decision-making process. The identified risk factors can expedite the preventative actions needed for tracking the debilitating chronic diseases and associated co-morbidities. For instance, chronic and long-term unmanaged risk factors such as obesity, diabetes, hypertension, and hyperlipidaemia can lead to coronary heart disease (also known as CAD (coronary artery disease), a leading cause of death worldwide. The costs involved in managing chronic diseases including diabetes, hypertension, hyperlipidaemia, which are the risk factors leading to CAD are significantly high, placing an enormous burden of disease on healthcare systems worldwide. Hence, there are several national clinical guidelines on CAD risk assessment, by monitoring, detection, and tracking of risk factors, such as smoking behavior, obesity and lifestyle factors, diabetes, and other co-morbid conditions, and calculation and tracking of the coronary risk scores. With the rapid adoption of electronic health record (EHR) systems, most patient data are stored in de-identified electronic format. Due to the involvement of multidisciplinary care teams in managing chronic diseases, and tracking the CAD risk factors, vital health data specific to a patient, are difficult to obtain, as they are scattered across various systems in various formats. Most of the time, the main data required for determining coronary risk are buried in unstructured clinical narratives and hand-over notes, often stored in a de-identified format. Existing solutions for chronic disease surveillance, involving detection and identification of the disease status and risk factors from the de-identified records, are based on manual methods requiring a significant amount of human efforts and domain expertise. Further, information extraction performed manually from the de-identified clinical records and text-based narratives can be error-prone, expensive, and prohibitively time-consuming. The key elements for detection and tracking of the disease status are often embedded in de-identified clinical records, discharge notes, and summaries as free text notes. Since these texts notes and records contain private information about the patients, including the personal and disease-related information, they normally exist in protected form, or in a de-identified format, normally referred to as PHI (protected health information) indicators, and are often embedded within the clinical text containing information needed to detect and track the risk factors associated with the disease. While the de-identification process itself is highly complicated, the chronic disease surveillance, involving the tasks of understanding and making sense out of de-identified clinical text notes (embedded with PHIs and other clinical information), and extracting meaningful information in terms of symptoms, risk factors, disease indicators, events, medications, allergic reactions, needed for monitoring and tracking the disease status is more complex and challenging. This is due to the difficulties associated with extracting linguistic and semantic relationships between PHIs and disease status when the clinical text records are in a de-identified unstructured form (as clinical notes and narratives). As a typical clinical discharge summary or text notes comprise several PHIs at the same time, it is more difficult to make sense out of deidentified medical text records with several masked PHIs embedded in it, with loss of context and structure in this embedded text, and the inability of traditional natural language processing (NLP) and text mining techniques to perform well. In this thesis, the focus is on detecting the vital risk factors and associated chronic disease conditions using de-identified medical text records, based on traditional machine learning and novel deep learning techniques. A novel computational framework for disease detection model development based on different machine learning models was proposed in this thesis. As it is extremely difficult to access the de-identified medical records from hospital systems, the NLP challenge shared tasks and associated benchmark datasets made available by the i2b2 consortium provide an opportunity to researchers in the computing and information technology field, to compare their research findings, and demonstrate the extension of previous work undertaken on several datasets provided by the i2b2 consortium, and share the outcomes and result in improving the state of the art. For a baseline comparison, the traditional approaches proposed in earlier work reported in the literature are compared with novel contributions made in this work. Most of the earlier work reported in the literature, that use i2b2 NLP challenge task datasets for experimental evaluation, are based on manual approaches, requiring human experts with domain knowledge from several multidisciplinary fields, including clinical, computing, natural language processing, and linguistics, and making sense of the de-identified clinical text notes and extracting knowledge, and building computer-based models based on this workflow a complex endeavor and very challenging. In the initial exploratory stages of this thesis, a sentence level segmentation approach for building disease status detection models based on shallow machine learning approaches, using PART, Naïve Bayes, Random Forest, and Hoeffding tree algorithms as the first step, and this served as the baseline reference for rest of the innovative algorithms for disease detection models to be developed in the rest of the thesis. The next step was the development of a document-level segmentation approach using more efficient and established shallow learning approaches, with the Random Forest, Naïve Bayes, Logistic Regression, and Gradient Boost Classifier algorithms. The findings from this stage helped in addressing the imbalanced and sparse data problem, as using algorithms based on ensemble techniques are well known to perform well in other application contexts in engineering and astronomy and have indeed led to enhanced performance of disease detection models. Then, these robust models based on ensemble techniques were extended for investigating the impact of multiple co-morbid disease conditions on debilitating cardiovascular disease risk assessment and examined with a new set of evaluation metrics for assessing improvement in performance and robustness, including accuracy, precision, recall, F1-score, confusion matrix, and ROC-AUC measures. As extraction of useful td-idf text features from de-identified clinical texts, particularly with clinical text data containing markers for multiple co-morbidities, became increasingly difficult, the use of new deep learning models was introduced. Since the deep learning models do not require feature engineering, the reliance on td-idf features reduced. Four new models based on deep machine learning and document level classification were proposed as the next step in enhancing the efficacy of disease detection models, including Bidirectional LSTM(BI-LSTM), CNN, Bidirectional GRU (BI-GRU), and BILSTM-BIGRU cascade models. Also, a new performance metric, in terms of micro-averaged F1-score was used, which has the capability to provide a better evaluation of machine learning models with the class imbalance and sparse data. Finally, a sentence-level classification approach with these deep learning algorithms was proposed, leading to enhanced performance assessed in terms of micro-averaged F1-scores. This incremental development, enhancement, and refinement of the proposed AI-based deep learning computational framework, and its experimental evaluation was done with several benchmarks publicly available clinical NLP i2b2 shared task challenge datasets, leading to significant performance improvement and robustness as compared to other competing methods and systems in the challenge tasks organized by i2b2 consortia.

Download or read book AI in Disease Detection written by Rajesh Singh and published by Wiley-IEEE Press. This book was released on 2025-01-09 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensive resource encompassing recent developments, current use cases, and future opportunities of AI in disease detection AI in Disease Detection discusses the integration of artificial intelligence to revolutionize disease detection approaches, with case studies of AI in disease detection as well as insight into the opportunities and challenges of AI in healthcare as a whole. The book explores a wide range of individual AI components such as computer vision, natural language processing, and machine learning as well as the development and implementation of AI systems for efficient practices in data collection, model training, and clinical validation. This book assists readers in assessing big data in healthcare and determining the drawbacks and possibilities associated with the implementation of AI in disease detection; categorizing major applications of AI in disease detection such as cardiovascular disease detection, cancer diagnosis, neurodegenerative disease detection, and infectious disease control, as well as implementing distinct AI methods and algorithms with medical data including patient records and medical images, and understanding the ethical and social consequences of AI in disease detection such as confidentiality, bias, and accessibility to healthcare. Sample topics explored in AI in Disease Detection include: Legal implication of AI in healthcare, with approaches to ensure privacy and security of patients and their data Identification of new biomarkers for disease detection, prediction of disease outcomes, and customized treatment plans depending on patient characteristics AI's role in disease surveillance and outbreak detection, with case studies of its current usage in real-world scenarios Clinical validation processes for AI disease detection models and how they can be validated for accuracy and effectiveness Delivering excellent coverage of the subject, AI in Disease Detection is an essential up-to-date reference for students, healthcare professionals, academics, and practitioners seeking to understand the possible applications of AI in disease detection and stay on the cutting edge of the most recent breakthroughs in the field.

Download or read book Research Anthology on Artificial Intelligence Applications in Security written by Management Association, Information Resources and published by IGI Global. This book was released on 2020-11-27 with total page 2253 pages. Available in PDF, EPUB and Kindle. Book excerpt: As industries are rapidly being digitalized and information is being more heavily stored and transmitted online, the security of information has become a top priority in securing the use of online networks as a safe and effective platform. With the vast and diverse potential of artificial intelligence (AI) applications, it has become easier than ever to identify cyber vulnerabilities, potential threats, and the identification of solutions to these unique problems. The latest tools and technologies for AI applications have untapped potential that conventional systems and human security systems cannot meet, leading AI to be a frontrunner in the fight against malware, cyber-attacks, and various security issues. However, even with the tremendous progress AI has made within the sphere of security, it’s important to understand the impacts, implications, and critical issues and challenges of AI applications along with the many benefits and emerging trends in this essential field of security-based research. Research Anthology on Artificial Intelligence Applications in Security seeks to address the fundamental advancements and technologies being used in AI applications for the security of digital data and information. The included chapters cover a wide range of topics related to AI in security stemming from the development and design of these applications, the latest tools and technologies, as well as the utilization of AI and what challenges and impacts have been discovered along the way. This resource work is a critical exploration of the latest research on security and an overview of how AI has impacted the field and will continue to advance as an essential tool for security, safety, and privacy online. This book is ideally intended for cyber security analysts, computer engineers, IT specialists, practitioners, stakeholders, researchers, academicians, and students interested in AI applications in the realm of security research.

Download or read book Computational Intelligence Methods in COVID 19 Surveillance Prevention Prediction and Diagnosis written by Khalid Raza and published by Springer Nature. This book was released on 2020-10-16 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: The novel coronavirus disease 2019 (COVID-19) pandemic has posed a major threat to human life and health. This book is beneficial for interdisciplinary students, researchers, and professionals to understand COVID-19 and how computational intelligence can be used for the purpose of surveillance, control, prevention, prediction, diagnosis, and potential treatment of the disease. The book contains different aspects of COVID-19 that includes fundamental knowledge, epidemic forecast models, surveillance and tracking systems, IoT- and IoMT-based integrated systems for COVID-19, social network analysis systems for COVID-19, radiological images (CT, X-ray) based diagnosis system, and computational intelligence and in silico drug design and drug repurposing methods against COVID-19 patients. The contributing authors of this volume are experts in their fields and they are from various reputed universities and institutions across the world. This volume is a valuable and comprehensive resource for computer and data scientists, epidemiologists, radiologists, doctors, clinicians, pharmaceutical professionals, along with graduate and research students of interdisciplinary and multidisciplinary sciences.

Download or read book Tracking and Preventing Diseases with Artificial Intelligence written by Mayuri Mehta and published by Springer Nature. This book was released on 2021 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents an overview of how machine learning and data mining techniques are used for tracking and preventing diseases. It covers several aspects such as stress level identification of a person from his/her speech, automatic diagnosis of disease from X-ray images, intelligent diagnosis of Glaucoma from clinical eye examination data, prediction of protein-coding genes from big genome data, disease detection through microscopic analysis of blood cells, information retrieval from electronic medical record using named entity recognition approaches, and prediction of drug-target interactions. The book is suitable for computer scientists having a bachelor degree in computer science. The book is an ideal resource as a reference book for teaching a graduate course on AI for Medicine or AI for Health care. Researchers working in the multidisciplinary areas use this book to discover the current developments. Besides its use in academia, this book provides enough details about the state-of-the-art algorithms addressing various biomedical domains, so that it could be used by industry practitioners who want to implement AI techniques to analyze the diseases. Medical institutions use this book as reference material and give tutorials to medical experts on how the advanced AI and ML techniques contribute to the diagnosis and prediction of the diseases.

Download or read book System Design for Epidemics Using Machine Learning and Deep Learning written by G. R. Kanagachidambaresan and published by Springer Nature. This book was released on 2023-02-01 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores the benefits of deploying Machine Learning (ML) and Artificial Intelligence (AI) in the health care environment. The authors study different research directions that are working to serve challenges faced in building strong healthcare infrastructure with respect to the pandemic crisis. The authors take note of obstacles faced in the rush to develop and alter technologies during the Covid crisis. They study what can be learned from them and what can be leveraged efficiently. The authors aim to show how healthcare providers can use technology to exploit advances in machine learning and deep learning in their own applications. Topics include remote patient monitoring, data analysis of human behavioral patterns, and machine learning for decision making in real-time.

Download or read book AI Applications for Disease Diagnosis and Treatment written by El Ouazzani, Rajae and published by IGI Global. This book was released on 2022-06-10 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence (AI) technology has been very successful across fields such as healthcare, security, precision agriculture, smart city, and autonomous driving and promises numerous benefits for social development, economic growth, wellbeing management, and human healthcare. Various intelligent healthcare applications have been created in order to assist patient healthcare and must be studied further. AI Applications for Disease Diagnosis and Treatment provides the current advances and applications of artificial intelligence applications in healthcare such as disease diagnosis, diet proposal, drug prescription and tracking, and physical and psychological assistance. Covering topics such as assistive healthcare, robotics, and machine learning, it is ideal for healthcare professionals, researchers, data analysts, academicians, practitioners, scholars, instructors, and students.

Download or read book Pre Screening Systems for Early Disease Prediction Detection and Prevention written by Edoh, Thierry and published by IGI Global. This book was released on 2018-10-26 with total page 419 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the development of advanced screening procedures and techniques, certain limitations of the existing screening processes for disease methodologies and paradigms have been noted. More accurate and less invasive screening methods are needed to diagnose and treat health disorders and diseases before symptoms appear. Pre-Screening Systems for Early Disease Prediction, Detection, and Prevention is a pivotal reference source that utilizes advanced ICT techniques to solve problems in health data collection, analysis, and interpretation, as well as improve existing health systems for the advanced screening of diseases. Using non-invasive biomedical sensor devices and internet of things technology, this book examines safer methods to accelerate disease detection and effectively treat patients while challenging previously used pre-screening processes. While highlighting topics such as the applications of machine learning, patient safety, diagnostics models, and condition management, this publication is ideally designed for healthcare specialists, researchers in health informatics, industry practitioners, and academics.

Download or read book Deep Learning in Medical Image Analysis written by R. Indrakumari and published by CRC Press. This book was released on 2024-07-10 with total page 197 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is designed as a reference text and provides a comprehensive overview of conceptual and practical knowledge about deep learning in medical image processing techniques. The post-pandemic situation teaches us the importance of doctors, medical analysis, and diagnosis of diseases in a rapid manner. This book provides a snapshot of the state of current research between deep learning, medical image processing, and health care with special emphasis on saving human life. The chapters cover a range of advanced technologies related to patient health monitoring, predicting diseases from genomic data, detecting artefactual events in vital signs monitoring data, and managing chronic diseases. This book Delivers an ideal introduction to image processing in medicine, emphasizing the clinical relevance and special requirements of the field Presents key principles by implementing algorithms from scratch and using simple MATLAB®/Octave scripts with image data Provides an overview of the physics of medical image processing alongside discussing image formats and data storage, intensity transforms, filtering of images and applications of the Fourier transform, three-dimensional spatial transforms, volume rendering, image registration, and tomographic reconstruction Highlights the new potential applications of machine learning techniques to the solution of important problems in biomedical image applications This book is for students, scholars, and professionals of biomedical technology and healthcare data analytics.

Download or read book Artificial Intelligence and Machine Learning in Healthcare written by Ankur Saxena and published by Springer Nature. This book was released on 2021-05-06 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reviews the application of artificial intelligence and machine learning in healthcare. It discusses integrating the principles of computer science, life science, and statistics incorporated into statistical models using existing data, discovering patterns in data to extract the information, and predicting the changes and diseases based on this data and models. The initial chapters of the book cover the practical applications of artificial intelligence for disease prognosis & management. Further, the role of artificial intelligence and machine learning is discussed with reference to specific diseases like diabetes mellitus, cancer, mycobacterium tuberculosis, and Covid-19. The chapters provide working examples on how different types of healthcare data can be used to develop models and predict diseases using machine learning and artificial intelligence. The book also touches upon precision medicine, personalized medicine, and transfer learning, with the real examples. Further, it also discusses the use of machine learning and artificial intelligence for visualization, prediction, detection, and diagnosis of Covid -19. This book is a valuable source of information for programmers, healthcare professionals, and researchers interested in understanding the applications of artificial intelligence and machine learning in healthcare.

Download or read book Internet of Healthcare Things written by Kavita Sharma and published by John Wiley & Sons. This book was released on 2022-01-28 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: INTERNET OF HEALTHCARE THINGS The book addresses privacy and security issues providing solutions through authentication and authorization mechanisms, blockchain, fog computing, machine learning algorithms, so that machine learning-enabled IoT devices can deliver information concealed in data for fast, computerized responses and enhanced decision-making. The main objective of this book is to motivate healthcare providers to use telemedicine facilities for monitoring patients in urban and rural areas and gather clinical data for further research. To this end, it provides an overview of the Internet of Healthcare Things (IoHT) and discusses one of the major threats posed by it, which is the data security and data privacy of health records. Another major threat is the combination of numerous devices and protocols, precision time, data overloading, etc. In the IoHT, multiple devices are connected and communicate through certain protocols. Therefore, the application of emerging technologies to mitigate these threats and provide secure data communication over the network is discussed. This book also discusses the integration of machine learning with the IoHT for analyzing huge amounts of data for predicting diseases more accurately. Case studies are also given to verify the concepts presented in the book. Audience Researchers and industry engineers in computer science, artificial intelligence, healthcare sector, IT professionals, network administrators, cybersecurity experts.