Health Informatics: A Computational Perspective in Healthcare PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Health Informatics: A Computational Perspective in Healthcare PDF full book. Access full book title Health Informatics: A Computational Perspective in Healthcare by Ripon Patgiri. Download full books in PDF and EPUB format.
Author: Ripon Patgiri
Publisher: Springer Nature
ISBN: 9811597359
Category : Technology & Engineering
Languages : en
Pages : 384
Get Book Here
Book Description
This book presents innovative research works to demonstrate the potential and the advancements of computing approaches to utilize healthcare centric and medical datasets in solving complex healthcare problems. Computing technique is one of the key technologies that are being currently used to perform medical diagnostics in the healthcare domain, thanks to the abundance of medical data being generated and collected. Nowadays, medical data is available in many different forms like MRI images, CT scan images, EHR data, test reports, histopathological data and doctor patient conversation data. This opens up huge opportunities for the application of computing techniques, to derive data-driven models that can be of very high utility, in terms of providing effective treatment to patients. Moreover, machine learning algorithms can uncover hidden patterns and relationships present in medical datasets, which are too complex to uncover, if a data-driven approach is not taken. With the help of computing systems, today, it is possible for researchers to predict an accurate medical diagnosis for new patients, using models built from previous patient data. Apart from automatic diagnostic tasks, computing techniques have also been applied in the process of drug discovery, by which a lot of time and money can be saved. Utilization of genomic data using various computing techniques is another emerging area, which may in fact be the key to fulfilling the dream of personalized medications. Medical prognostics is another area in which machine learning has shown great promise recently, where automatic prognostic models are being built that can predict the progress of the disease, as well as can suggest the potential treatment paths to get ahead of the disease progression.
Author: Ripon Patgiri
Publisher: Springer Nature
ISBN: 9811597359
Category : Technology & Engineering
Languages : en
Pages : 384
Get Book Here
Book Description
This book presents innovative research works to demonstrate the potential and the advancements of computing approaches to utilize healthcare centric and medical datasets in solving complex healthcare problems. Computing technique is one of the key technologies that are being currently used to perform medical diagnostics in the healthcare domain, thanks to the abundance of medical data being generated and collected. Nowadays, medical data is available in many different forms like MRI images, CT scan images, EHR data, test reports, histopathological data and doctor patient conversation data. This opens up huge opportunities for the application of computing techniques, to derive data-driven models that can be of very high utility, in terms of providing effective treatment to patients. Moreover, machine learning algorithms can uncover hidden patterns and relationships present in medical datasets, which are too complex to uncover, if a data-driven approach is not taken. With the help of computing systems, today, it is possible for researchers to predict an accurate medical diagnosis for new patients, using models built from previous patient data. Apart from automatic diagnostic tasks, computing techniques have also been applied in the process of drug discovery, by which a lot of time and money can be saved. Utilization of genomic data using various computing techniques is another emerging area, which may in fact be the key to fulfilling the dream of personalized medications. Medical prognostics is another area in which machine learning has shown great promise recently, where automatic prognostic models are being built that can predict the progress of the disease, as well as can suggest the potential treatment paths to get ahead of the disease progression.
Author: Om Prakash Jena
Publisher: John Wiley & Sons
ISBN: 1119818680
Category : Computers
Languages : en
Pages : 434
Get Book Here
Book Description
COMPUTATIONAL INTELLIGENCE and HEALTHCARE INFORMATICS The book provides the state-of-the-art innovation, research, design, and implements methodological and algorithmic solutions to data processing problems, designing and analysing evolving trends in health informatics, intelligent disease prediction, and computer-aided diagnosis. Computational intelligence (CI) refers to the ability of computers to accomplish tasks that are normally completed by intelligent beings such as humans and animals. With the rapid advance of technology, artificial intelligence (AI) techniques are being effectively used in the fields of health to improve the efficiency of treatments, avoid the risk of false diagnoses, make therapeutic decisions, and predict the outcome in many clinical scenarios. Modern health treatments are faced with the challenge of acquiring, analyzing and applying the large amount of knowledge necessary to solve complex problems. Computational intelligence in healthcare mainly uses computer techniques to perform clinical diagnoses and suggest treatments. In the present scenario of computing, CI tools present adaptive mechanisms that permit the understanding of data in difficult and changing environments. The desired results of CI technologies profit medical fields by assembling patients with the same types of diseases or fitness problems so that healthcare facilities can provide effectual treatments. This book starts with the fundamentals of computer intelligence and the techniques and procedures associated with it. Contained in this book are state-of-the-art methods of computational intelligence and other allied techniques used in the healthcare system, as well as advances in different CI methods that will confront the problem of effective data analysis and storage faced by healthcare institutions. The objective of this book is to provide researchers with a platform encompassing state-of-the-art innovations; research and design; implementation of methodological and algorithmic solutions to data processing problems; and the design and analysis of evolving trends in health informatics, intelligent disease prediction and computer-aided diagnosis. Audience The book is of interest to artificial intelligence and biomedical scientists, researchers, engineers and students in various settings such as pharmaceutical & biotechnology companies, virtual assistants developing companies, medical imaging & diagnostics centers, wearable device designers, healthcare assistance robot manufacturers, precision medicine testers, hospital management, and researchers working in healthcare system.
Author: Ripon Patgiri
Publisher:
ISBN: 9789811597367
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
This book presents innovative research works to demonstrate the potential and the advancements of computing approaches to utilize healthcare centric and medical datasets in solving complex healthcare problems. Computing technique is one of the key technologies that are being currently used to perform medical diagnostics in the healthcare domain, thanks to the abundance of medical data being generated and collected. Nowadays, medical data is available in many different forms like MRI images, CT scan images, EHR data, test reports, histopathological data and doctor patient conversation data. This opens up huge opportunities for the application of computing techniques, to derive data-driven models that can be of very high utility, in terms of providing effective treatment to patients. Moreover, machine learning algorithms can uncover hidden patterns and relationships present in medical datasets, which are too complex to uncover, if a data-driven approach is not taken. With the help of computing systems, today, it is possible for researchers to predict an accurate medical diagnosis for new patients, using models built from previous patient data. Apart from automatic diagnostic tasks, computing techniques have also been applied in the process of drug discovery, by which a lot of time and money can be saved. Utilization of genomic data using various computing techniques is another emerging area, which may in fact be the key to fulfilling the dream of personalized medications. Medical prognostics is another area in which machine learning has shown great promise recently, where automatic prognostic models are being built that can predict the progress of the disease, as well as can suggest the potential treatment paths to get ahead of the disease progression. .
Author: Vishal Jain
Publisher: Springer Nature
ISBN: 3030408507
Category : Technology & Engineering
Languages : en
Pages : 418
Get Book Here
Book Description
This unique book introduces a variety of techniques designed to represent, enhance and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. Providing a unique compendium of current and emerging machine learning paradigms for healthcare informatics, it reflects the diversity, complexity, and the depth and breadth of this multi-disciplinary area. Further, it describes techniques for applying machine learning within organizations and explains how to evaluate the efficacy, suitability, and efficiency of such applications. Featuring illustrative case studies, including how chronic disease is being redefined through patient-led data learning, the book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare challenges.
Author: Rishabha Malviya
Publisher: CRC Press
ISBN: 1003810330
Category : Business & Economics
Languages : en
Pages : 81
Get Book Here
Book Description
This book provides a multidisciplinary overview of the design and implementation of systems for remote patient monitoring and healthcare. Readers are guided through the components of such a system and shown how they could be integrated into a coherent framework for deployment in practice. The book ends with a discussion of case studies to provide practical examples and design methods for remote healthcare systems with different needs. This book contains information that will be helpful to undergraduate and postgraduate students, academic and professional researchers, and individuals who employ computational intelligence. By presenting new models of care and support, AI in medicine is having an impact on the entire value chain of clinical practice and the patient care delivery system. This is especially evident in telemedicine breakthroughs, where applications of AI are utilized to assist, complement, or create new forms of remote healthcare. Through the use of communications technology, telemedicine enables medical professionals to examine, diagnose, and treat patients remotely. Now, the technology might also integrate online diagnostics, etc., to increase the effectiveness of the healthcare system and enhance patient outcomes. By enabling elderly people to live independently, technological advancements in fields like remote patient monitoring (RPM) systems can revolutionize senior care. Medical diagnosis will improve, and remote patient monitoring will become simpler thanks to artificial intelligence. The resistance of many patients and doctors to using the telemedicine idea for medical visits will probably be overcome by AI automation.
Author: Andreas Holzinger
Publisher: Springer
ISBN: 3319504789
Category : Computers
Languages : en
Pages : 503
Get Book Here
Book Description
Machine learning (ML) is the fastest growing field in computer science, and Health Informatics (HI) is amongst the greatest application challenges, providing future benefits in improved medical diagnoses, disease analyses, and pharmaceutical development. However, successful ML for HI needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization. Tackling complex challenges needs both disciplinary excellence and cross-disciplinary networking without any boundaries. Following the HCI-KDD approach, in combining the best of two worlds, it is aimed to support human intelligence with machine intelligence. This state-of-the-art survey is an output of the international HCI-KDD expert network and features 22 carefully selected and peer-reviewed chapters on hot topics in machine learning for health informatics; they discuss open problems and future challenges in order to stimulate further research and international progress in this field.
Author: Pawan Singh Mehra
Publisher: CRC Press
ISBN: 1000541479
Category : Computers
Languages : en
Pages : 267
Get Book Here
Book Description
This book covers the fundamentals of IoT and healthcare systems for carrying out system architectures, protocols, wearable devices, and interoperability. It explores major challenges in artificial intelligence (AI) and smart computing in resource-constrained IoT-based applications along with cost, energy efficiency, and the availability of quality service. Healthcare Systems and Health Informatics: Using Internet of Things explores the role of AI and smart computing in health informatics and healthcare with an emphasis on clinical data management and analysis for precise prediction and prompt action. It presents cutting-edge tracking, monitoring, real-time assistance, and security for IoT in healthcare and broadly discusses wearable sensors and IoT devices and their role in smart living assistance. The book goes on to describe a system model and architecture for a clear picture of energy conservation–based IoT in healthcare and explains the challenges and opportunities with IoT-based healthcare industries. A study of the threats and impacts, along with the need for information security, is also included. The chapters are written by experts in the field, and this book provides a comprehensive description of the important aspects of IoT and health from a beginner- to advanced-level perspective and is ideal for researchers, academicians, students, persons in industry, technologists, and entrepreneurs.
Author: Qiang Yang
Publisher: Springer Nature
ISBN: 3030630765
Category : Computers
Languages : en
Pages : 291
Get Book Here
Book Description
This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated learning model against different types of attacks such as data leakage and/or data poisoning. Secondly, the book presents incentive mechanisms which aim to encourage individuals to participate in the federated learning ecosystems. Last but not least, this book also describes how federated learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both the academia and the industry, who would like to learn about federated learning, practice its implementation, and apply it in their own business. Readers are expected to have some basic understanding of linear algebra, calculus, and neural network. Additionally, domain knowledge in FinTech and marketing would be helpful.”
Author: Patrick W. O'Carroll
Publisher: Springer Science & Business Media
ISBN: 0387227458
Category : Medical
Languages : en
Pages : 814
Get Book Here
Book Description
Let us not go over the old ground, let us rather prepare for what is to come. —Marcus Tullius Cicero Improvements in the health status of communities depend on effective public health and healthcare infrastructures. These infrastructures are increasingly electronic and tied to the Internet. Incorporating emerging technologies into the service of the community has become a required task for every public health leader. The revolution in information technology challenges every sector of the health enterprise. Individuals, care providers, and public health agencies can all benefit as we reshape public health through the adoption of new infor- tion systems, use of electronic methods for disease surveillance, and refor- tion of outmoded processes. However, realizing the benefits will be neither easy nor inexpensive. Technological innovation brings the promise of new ways of improving health. Individuals have become more involved in knowing about, and managing and improving, their own health through Internet access. Similarly, healthcare p- viders are transforming the ways in which they assess, treat, and document - tient care through their use of new technologies. For example, point-of-care and palm-type devices will soon be capable of uniquely identifying patients, s- porting patient care, and documenting treatment simply and efficiently.
Author: Amit Kumar Tyagi
Publisher: John Wiley & Sons
ISBN: 1119785723
Category : Computers
Languages : en
Pages : 532
Get Book Here
Book Description
The book details deep learning models like ANN, RNN, LSTM, in many industrial sectors such as transportation, healthcare, military, agriculture, with valid and effective results, which will help researchers find solutions to their deep learning research problems. We have entered the era of smart world devices, where robots or machines are being used in most applications to solve real-world problems. These smart machines/devices reduce the burden on doctors, which in turn make their lives easier and the lives of their patients better, thereby increasing patient longevity, which is the ultimate goal of computer vision. Therefore, the goal in writing this book is to attempt to provide complete information on reliable deep learning models required for e-healthcare applications. Ways in which deep learning can enhance healthcare images or text data for making useful decisions are discussed. Also presented are reliable deep learning models, such as neural networks, convolutional neural networks, backpropagation, and recurrent neural networks, which are increasingly being used in medical image processing, including for colorization of black and white X-ray images, automatic machine translation images, object classification in photographs/images (CT scans), character or useful generation (ECG), image caption generation, etc. Hence, reliable deep learning methods for the perception or production of better results are a necessity for highly effective e-healthcare applications. Currently, the most difficult data-related problem that needs to be solved concerns the rapid increase of data occurring each day via billions of smart devices. To address the growing amount of data in healthcare applications, challenges such as not having standard tools, efficient algorithms, and a sufficient number of skilled data scientists need to be overcome. Hence, there is growing interest in investigating deep learning models and their use in e-healthcare applications. Audience Researchers in artificial intelligence, big data, computer science, and electronic engineering, as well as industry engineers in transportation, healthcare, biomedicine, military, agriculture.
