Optimized Predictive Models In Health Care Using Machine Learning
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Optimized Predictive Models In Health Care Using Machine Learning
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Author : Sandeep Kumar
language : en
Publisher: John Wiley & Sons
Release Date : 2024-03-12
Optimized Predictive Models In Health Care Using Machine Learning written by Sandeep Kumar and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-12 with Computers categories.
OPTIMIZED PREDICTIVE MODELS IN HEALTH CARE USING MACHINE LEARNING This book is a comprehensive guide to developing and implementing optimized predictive models in healthcare using machine learning and is a required resource for researchers, healthcare professionals, and students who wish to know more about real-time applications. The book focuses on how humans and computers interact to ever-increasing levels of complexity and simplicity and provides content on the theory of optimized predictive model design, evaluation, and user diversity. Predictive modeling, a field of machine learning, has emerged as a powerful tool in healthcare for identifying high-risk patients, predicting disease progression, and optimizing treatment plans. By leveraging data from various sources, predictive models can help healthcare providers make informed decisions, resulting in better patient outcomes and reduced costs. Other essential features of the book include: provides detailed guidance on data collection and preprocessing, emphasizing the importance of collecting accurate and reliable data; explains how to transform raw data into meaningful features that can be used to improve the accuracy of predictive models; gives a detailed overview of machine learning algorithms for predictive modeling in healthcare, discussing the pros and cons of different algorithms and how to choose the best one for a specific application; emphasizes validating and evaluating predictive models; provides a comprehensive overview of validation and evaluation techniques and how to evaluate the performance of predictive models using a range of metrics; discusses the challenges and limitations of predictive modeling in healthcare; highlights the ethical and legal considerations that must be considered when developing predictive models and the potential biases that can arise in those models. Audience The book will be read by a wide range of professionals who are involved in healthcare, data science, and machine learning.
Leveraging Data Science For Global Health
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Author : Leo Anthony Celi
language : en
Publisher: Springer Nature
Release Date : 2020-07-31
Leveraging Data Science For Global Health written by Leo Anthony Celi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-31 with Medical categories.
This open access book explores ways to leverage information technology and machine learning to combat disease and promote health, especially in resource-constrained settings. It focuses on digital disease surveillance through the application of machine learning to non-traditional data sources. Developing countries are uniquely prone to large-scale emerging infectious disease outbreaks due to disruption of ecosystems, civil unrest, and poor healthcare infrastructure – and without comprehensive surveillance, delays in outbreak identification, resource deployment, and case management can be catastrophic. In combination with context-informed analytics, students will learn how non-traditional digital disease data sources – including news media, social media, Google Trends, and Google Street View – can fill critical knowledge gaps and help inform on-the-ground decision-making when formal surveillance systems are insufficient.
Genomics At The Nexus Of Ai Computer Vision And Machine Learning
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Author : Shilpa Choudhary
language : en
Publisher: John Wiley & Sons
Release Date : 2024-11-05
Genomics At The Nexus Of Ai Computer Vision And Machine Learning written by Shilpa Choudhary and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-05 with Computers categories.
The book provides a comprehensive understanding of cutting-edge research and applications at the intersection of genomics and advanced AI techniques and serves as an essential resource for researchers, bioinformaticians, and practitioners looking to leverage genomics data for AI-driven insights and innovations. The book encompasses a wide range of topics, starting with an introduction to genomics data and its unique characteristics. Each chapter unfolds a unique facet, delving into the collaborative potential and challenges that arise from advanced technologies. It explores image analysis techniques specifically tailored for genomic data. It also delves into deep learning showcasing the power of convolutional neural networks (CNN) and recurrent neural networks (RNN) in genomic image analysis and sequence analysis. Readers will gain practical knowledge on how to apply deep learning techniques to unlock patterns and relationships in genomics data. Transfer learning, a popular technique in AI, is explored in the context of genomics, demonstrating how knowledge from pre-trained models can be effectively transferred to genomic datasets, leading to improved performance and efficiency. Also covered is the domain adaptation techniques specifically tailored for genomics data. The book explores how genomics principles can inspire the design of AI algorithms, including genetic algorithms, evolutionary computing, and genetic programming. Additional chapters delve into the interpretation of genomic data using AI and ML models, including techniques for feature importance and visualization, as well as explainable AI methods that aid in understanding the inner workings of the models. The applications of genomics in AI span various domains, and the book explores AI-driven drug discovery and personalized medicine, genomic data analysis for disease diagnosis and prognosis, and the advancement of AI-enabled genomic research. Lastly, the book addresses the ethical considerations in integrating genomics with AI, computer vision, and machine learning. Audience The book will appeal to biomedical and computer/data scientists and researchers working in genomics and bioinformatics seeking to leverage AI, computer vision, and machine learning for enhanced analysis and discovery; healthcare professionals advancing personalized medicine and patient care; industry leaders and decision-makers in biotechnology, pharmaceuticals, and healthcare industries seeking strategic insights into the integration of genomics and advanced technologies.
Generative Artificial Intelligence For Biomedical And Smart Health Informatics
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Author : Aditya Khamparia
language : en
Publisher: John Wiley & Sons
Release Date : 2025-02-05
Generative Artificial Intelligence For Biomedical And Smart Health Informatics written by Aditya Khamparia and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-05 with Medical categories.
Enables readers to understand the future of medical applications with generative AI and related applications Generative Artificial Intelligence for Biomedical and Smart Health Informatics delivers a comprehensive overview of the most recent generative AI-driven medical applications based on deep learning and machine learning in which biomedical data is gathered, processed, and analyzed using data augmentation techniques. This book covers many applications of generative models for medical image data, including volumetric medical image segmentation, data augmentation, MRI reconstruction, and modeling of spatiotemporal medical data. The book explores findings obtained by explainable AI techniques, with coverage of various techniques rarely reported in literature. Throughout, feedback and user experiences from physicians and medical staff, as well as use cases, are included to provide important context. The book discusses topics including privacy and security challenges in AI-enabled health informatics, biosensor-guided AI interventions in personalized medicine, regulatory frameworks and guidelines for AI-based medical devices, education and training for building responsible AI solutions in healthcare, and challenges and opportunities in integrating generative AI with wearable devices. Topics covered include: Treatment of neurological disorders using intelligent techniques and image-guided and tomography interventions for neuromuscular disorders Bio-inspired smart healthcare service frameworks with AI, machine learning, and deep learning, integration of IoT devices, and edge computing in industrial and clinical systems Traffic management and optimization in distributed environments, patient data management, disease surveillance and prediction, and telemedicine and remote monitoring Education-driven, peer-to-peer, and service-oriented architectures and transparency and accountability in medical decision-making Generative Artificial Intelligence for Biomedical and Smart Health Informatics is an essential reference for computer science researchers, medical professionals, healthcare informatics, and medical imaging researchers interested in understanding the potential of artificial intelligence and other related technologies in healthcare.
Medinfo 2021 One World One Health Global Partnership For Digital Innovation
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Author : P. Otero
language : en
Publisher: IOS Press
Release Date : 2022-08-05
Medinfo 2021 One World One Health Global Partnership For Digital Innovation written by P. Otero and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-05 with Medical categories.
The World Health Organization defines health as “a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity”, and its constitution also asserts that health for all people is “dependent on the fullest co-operation of individuals and States”. The ongoing pandemic has highlighted the power of both healthy and unhealthy information, so while healthcare and public health services have depended upon timely and accurate data and continually updated knowledge, social media has shown how unhealthy misinformation can be spread and amplified, reinforcing existing prejudices, conspiracy theories and political biases. This book presents the proceedings of MedInfo 2021, the 18th World Congress of Medical and Health Informatics, held as a virtual event from 2-4 October 2021, with pre-recorded presentations for all accepted submissions. The theme of the conference was One World, One Health – Global Partnership for Digital Innovation and submissions were requested under 5 themes: information and knowledge management; quality, safety and outcomes; health data science; human, organizational and social aspects; and global health informatics. The Programme Committee received 352 submissions from 41 countries across all IMIA regions, and 147 full papers, 60 student papers and 79 posters were accepted for presentation after review and are included in these proceedings. Providing an overview of current work in the field over a wide range of disciplines, the book will be of interest to all those whose work involves some aspect of medical or health informatics.
Revolutionizing Ai With Brain Inspired Technology Neuromorphic Computing
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Author : Lilhore, Umesh Kumar
language : en
Publisher: IGI Global
Release Date : 2024-11-29
Revolutionizing Ai With Brain Inspired Technology Neuromorphic Computing written by Lilhore, Umesh Kumar and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-29 with Computers categories.
As artificial intelligence (AI) continues to evolve, neuromorphic computing stands at the forefront of this revolution, offering a transformative approach by mimicking the structure and function of the human brain. This cutting-edge technology is reshaping AI, making it more efficient, adaptive, and capable of complex tasks that were once thought impossible. Neuromorphic computing has the potential to revolutionize industries such as healthcare, robotics, and autonomous vehicles, driving advancements that could redefine the future of technology and its applications in everyday life. Understanding this emerging field is crucial for anyone involved in AI development or interested in the next frontier of technological innovation. Revolutionizing AI with Brain-Inspired Technology: Neuromorphic Computing covers neuromorphic computing, its real-world applications, and the latest advancements pushing the boundaries of AI. By offering a comprehensive overview and inspiring new research, this book plays a pivotal role in shaping the future of AI and its impact on various sectors. This volume is an essential resource for researchers, academics, professionals, and policymakers who seek to understand the principles and potential of neuromorphic computing as well as the societal implications of these technologies.
Bio Inspired Algorithms In Machine Learning And Deep Learning For Disease Detection
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Author : Balasubramaniam S
language : en
Publisher: CRC Press
Release Date : 2025-03-13
Bio Inspired Algorithms In Machine Learning And Deep Learning For Disease Detection written by Balasubramaniam S and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-13 with Technology & Engineering categories.
Currently, computational intelligence approaches are utilised in various science and engineering applications to analyse information, make decisions, and achieve optimisation goals. Over the past few decades, various techniques and algorithms have been created in disciplines such as genetic algorithms, artificial neural networks, evolutionary algorithms, and fuzzy algorithms. In the coming years, intelligent optimisation algorithms are anticipated to become more efficient in addressing various issues in engineering, scientific, medical, space, and artificial satellite fields, particularly in early disease diagnosis. A metaheuristic in computer science is designed to discover optimisation algorithms capable of solving intricate issues. Metaheuristics are optimisation algorithms that mimic biological behaviours of animals or birds and are utilised to discover the best solution for a certain problem. A meta-heuristic is an advanced approach used by heuristics to tackle intricate optimisation problems. A metaheuristic in mathematical programming is a method that seeks a solution to an optimisation problem. Metaheuristics utilise a heuristic function to assist in the search process. Heuristic search can be categorised as blind search or informed search. Meta-heuristic optimisation algorithms are gaining popularity in various applications due to their simplicity, independence from data trends, ability to find optimal solutions, and versatility across different fields. Recently, many nature-inspired computation algorithms have been utilised to diagnose people with different diseases. Nature-inspired methodologies are now widely utilised across several fields for tasks such as data analysis, decision-making, and optimisation. Techniques inspired by nature are categorised as either biology-based or natural phenomena-based. Bioinspired computing encompasses various topics in computer science, mathematics, and biology in recent years. Bio-inspired computer optimisation algorithms are a developing method that utilises concepts and inspiration from biological development to create new and resilient competitive strategies. Bio-inspired optimisation algorithms have gained recognition in machine learning and deep learning for solving complicated issues in science and engineering. Utilising BIAs learning methods with machine learning and deep learning shows great promise for accurately classifying medical conditions. This book explores the historical development of bio-inspired algorithms and their application in machine learning and deep learning models for disease diagnosis, including COVID-19, heart diseases, cancer, diabetes and some other diseases. It discusses the advantages of using bio-inspired algorithms in disease diagnosis and concludes with research directions and future prospects in this field.
Integrative Machine Learning And Optimization Algorithms For Disease Prediction
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Author : Muniasamy, Anandhavalli
language : en
Publisher: IGI Global
Release Date : 2025-07-03
Integrative Machine Learning And Optimization Algorithms For Disease Prediction written by Muniasamy, Anandhavalli and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-03 with Medical categories.
Integrative approaches that combine machine learning (ML) and optimization algorithms rapidly transform the landscape of disease prediction and healthcare analytics. By leveraging the predictive power of ML models alongside the efficiency of optimization techniques, researchers can develop more accurate, robust, and scalable systems for early diagnosis and risk assessment. These hybrid frameworks enable the integration of diverse data sources into cohesive predictive models. The synergy between ML and optimization enhances model performance while supporting personalized medicine by tailoring predictions to individual patient profiles. Integrative methodologies hold significant promises for advancing clinical decision-making and improving health outcomes. Integrative Machine Learning and Optimization Algorithms for Disease Prediction explores the cutting-edge applications of machine learning, deep learning, and optimization algorithms in disease prediction. It examines how diverse machine learning models, from traditional algorithms to deep learning and ensemble methods, can be optimized for high-stakes clinical predictions. This book covers topics such as disease prediction, healthcare data, and mental health, and is a useful resource for computer engineers, medical professionals, academicians, researchers, and scientists.
Optimized Computational Intelligence Driven Decision Making
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Author : Hrudaya Kumar Tripathy
language : en
Publisher: John Wiley & Sons
Release Date : 2024-07-30
Optimized Computational Intelligence Driven Decision Making written by Hrudaya Kumar Tripathy and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-30 with Computers categories.
This book covers a wide range of advanced techniques and approaches for designing and implementing computationally intelligent methods in different application domains which is of great use to not only researchers but also academicians and industry experts. Optimized Computational Intelligence (OCI) is a new, cutting-edge, and multidisciplinary research area that tackles the fundamental problems shared by modern informatics, biologically-inspired computation, software engineering, AI, cybernetics, cognitive science, medical science, systems science, philosophy, linguistics, economics, management science, and life sciences. OCI aims to apply modern computationally intelligent methods to generate optimum outcomes in various application domains. This book presents the latest technologies-driven material to explore optimized various computational intelligence domains. includes real-life case studies highlighting different advanced technologies in computational intelligence; provides a unique compendium of current and emerging hybrid intelligence paradigms for advanced informatics; reflects the diversity, complexity, and depth and breadth of this critical bio-inspired domain; offers a guided tour of computational intelligence algorithms, architecture design, and applications of learning in dealing with cognitive informatics challenges; presents a variety of intelligent and optimized techniques designed to represent, enhance, and empower multi-disciplinary and multi-institutional data analytics research in intelligent decision-making system dynamics; includes architectural models and applications-based augmented solutions for optimized computational intelligence. Audience The book will interest a range of engineers and researchers in information technology, computer science, and artificial intelligence working in the interdisciplinary field of computational intelligence.
Synergizing Data Envelopment Analysis And Machine Learning For Performance Optimization In Healthcare
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Author : Ajibesin, Adeyemi Abel
language : en
Publisher: IGI Global
Release Date : 2025-05-02
Synergizing Data Envelopment Analysis And Machine Learning For Performance Optimization In Healthcare written by Ajibesin, Adeyemi Abel and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-02 with Medical categories.
Healthcare systems face the challenge of delivering high-quality care while efficiently managing costs and resources. Traditional methods of performance evaluation often fall short when addressing the complex and diverse nature of healthcare operations. Data envelopment analysis (DEA) has been used to measure the efficiency of healthcare providers, but its linear, deterministic nature limits its adaptability to dynamic environments. In contrast, machine learning (ML) can handle complex, non-linear relationships and high-dimensional data, offering deeper insights and predictive capabilities. The synergy between DEA and ML presents an opportunity to overcome these limitations and drive more effective performance optimization. It leads to efficiency assessments through predictive analytics and improved resource allocation with data-driven insights and optimizing clinical pathways and decision support systems for better patient outcomes. Synergizing Data Envelopment Analysis and Machine Learning for Performance Optimization in Healthcare explores the integration of DEA and ML to enhance performance optimization in healthcare, improving efficiency, care quality, and resource management. It examines theoretical foundations, methodological innovations, and practical applications, providing a comprehensive resource with a key focus on development of algorithms to address challenges in healthcare optimization. Covering topics such as healthcare equipment manufacturing, human augmentation, and robotic surgery, this book is an excellent resource for hospital administrators, clinical managers, clinical decision-makers, policymakers, public health officials, professionals, researchers, scholars, academics, and more.