SPATIAL ANALYSIS IN PUBLIC HEALTH DOMAIN: AN NLP APPROACH
SPATIAL ANALYSIS IN PUBLIC HEALTH DOMAIN: AN NLP APPROACH

Author: Pattathal Vijayakumar Arun

Publisher: Infinite Study

Published:

Total Pages: 12

ISBN-13:

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Remote sensing products are effectively used as a tool for decision making in various fields, especially in medical research and health care analyses. GIS is particularly well suited in this context because of its spatial analysis and display capabilities. The integration of RS techniques in public health has been categorised as continuous and discrete strategies where latter is preferred. We have investigated the integration of these approaches through linguistic interpretation of images. In this paper, we propose a framework for direct natural language interpretation of satellite images using probabilistic grammar rules in conjunction with evolutionary computing techniques. Spectral and spatial information has been dynamically combined using adaptive kernel strategy for effective representation of the contextual knowledge. The developed methodology has been evaluated in different querying contexts and investigations revealed that considerable success has been achieved with the procedure. The methodology has also demonstrated to be effective in intelligent interpolation, automatic interpretation as well as attribute, topology, proximity, and semantic analyses.

Digital Health
Digital Health

Author: Homero Rivas

Publisher: Springer

Published: 2018-01-02

Total Pages: 372

ISBN-13: 3319614460

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This book presents a comprehensive state-of the-art approach to digital health technologies and practices within the broad confines of healthcare practices. It provides a canvas to discuss emerging digital health solutions, propelled by the ubiquitous availability of miniaturized, personalized devices and affordable, easy to use wearable sensors, and innovative technologies like 3D printing, virtual and augmented reality and driverless robots and vehicles including drones. One of the most significant promises the digital health solutions hold is to keep us healthier for longer, even with limited resources, while truly scaling the delivery of healthcare. Digital Health: Scaling Healthcare to the World addresses the emerging trends and enabling technologies contributing to technological advances in healthcare practice in the 21st Century. These areas include generic topics such as mobile health and telemedicine, as well as specific concepts such as social media for health, wearables and quantified-self trends. Also covered are the psychological models leveraged in design of solutions to persuade us to follow some recommended actions, then the design and educational facets of the proposed innovations, as well as ethics, privacy, security, and liability aspects influencing its acceptance. Furthermore, sections on economic aspects of the proposed innovations are included, analyzing the potential business models and entrepreneurship opportunities in the domain.

Negation and Speculation Detection
Negation and Speculation Detection

Author: Noa P. Cruz Díaz

Publisher: John Benjamins Publishing Company

Published: 2019-02-15

Total Pages: 107

ISBN-13: 9027262950

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Negation and speculation detection is an emerging topic that has attracted the attention of many researchers, and there is clearly a lack of relevant textbooks and survey texts. This book aims to define negation and speculation from a natural language processing perspective, to explain the need for processing these phenomena, to summarise existing research on processing negation and speculation, to provide a list of resources and tools, and to speculate about future developments in this research area. An advantage of this book is that it will not only provide an overview of the state of the art in negation and speculation detection, but will also introduce newly developed data sets and scripts. It will be useful for students of natural language processing subjects who are interested in understanding this task in more depth and for researchers with an interest in these phenomena in order to improve performance in other natural language processing tasks.

Deep Learning in Biomedical and Health Informatics
Deep Learning in Biomedical and Health Informatics

Author: M. A. Jabbar

Publisher: CRC Press

Published: 2021-09-26

Total Pages: 224

ISBN-13: 1000429083

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This book provides a proficient guide on the relationship between Artificial Intelligence (AI) and healthcare and how AI is changing all aspects of the healthcare industry. It also covers how deep learning will help in diagnosis and the prediction of disease spread. The editors present a comprehensive review of research applying deep learning in health informatics in the fields of medical imaging, electronic health records, genomics, and sensing, and highlights various challenges in applying deep learning in health care. This book also includes applications and case studies across all areas of AI in healthcare data. The editors also aim to provide new theories, techniques, developments, and applications of deep learning, and to solve emerging problems in healthcare and other domains. This book is intended for computer scientists, biomedical engineers, and healthcare professionals researching and developing deep learning techniques. In short, the volume : Discusses the relationship between AI and healthcare, and how AI is changing the health care industry. Considers uses of deep learning in diagnosis and prediction of disease spread. Presents a comprehensive review of research applying deep learning in health informatics across multiple fields. Highlights challenges in applying deep learning in the field. Promotes research in ddeep llearning application in understanding the biomedical process. Dr.. M.A. Jabbar is a professor and Head of the Department AI&ML, Vardhaman College of Engineering, Hyderabad, Telangana, India. Prof. (Dr.) Ajith Abraham is the Director of Machine Intelligence Research Labs (MIR Labs), Auburn, Washington, USA. Dr.. Onur Dogan is an assistant professor at İzmir Bakırçay University, Turkey. Prof. Dr. Ana Madureira is the Director of The Interdisciplinary Studies Research Center at Instituto Superior de Engenharia do Porto (ISEP), Portugal. Dr.. Sanju Tiwari is a senior researcher at Universidad Autonoma de Tamaulipas, Mexico.

Artificial Intelligence for the Internet of Health Things
Artificial Intelligence for the Internet of Health Things

Author: K. Shankar

Publisher: CRC Press

Published: 2021-05-10

Total Pages: 216

ISBN-13: 1000374297

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This book discusses research in Artificial Intelligence for the Internet of Health Things. It investigates and explores the possible applications of machine learning, deep learning, soft computing, and evolutionary computing techniques in design, implementation, and optimization of challenging healthcare solutions. This book features a wide range of topics such as AI techniques, IoT, cloud, wearables, and secured data transmission. Written for a broad audience, this book will be useful for clinicians, health professionals, engineers, technology developers, IT consultants, researchers, and students interested in the AI-based healthcare applications. Provides a deeper understanding of key AI algorithms and their use and implementation within the wider healthcare sector Explores different disease diagnosis models using machine learning, deep learning, healthcare data analysis, including machine learning, and data mining and soft computing algorithms Discusses detailed IoT, wearables, and cloud-based disease diagnosis model for intelligent systems and healthcare Reviews different applications and challenges across the design, implementation, and management of intelligent systems and healthcare data networks Introduces a new applications and case studies across all areas of AI in healthcare data K. Shankar (Member, IEEE) is a Postdoctoral Fellow of the Department of Computer Applications, Alagappa University, Karaikudi, India. Eswaran Perumal is an Assistant Professor of the Department of Computer Applications, Alagappa University, Karaikudi, India. Dr. Deepak Gupta is an Assistant Professor of the Department Computer Science & Engineering, Maharaja Agrasen Institute of Technology (GGSIPU), Delhi, India.

Artificial Intelligence for Data Science in Theory and Practice
Artificial Intelligence for Data Science in Theory and Practice

Author: Mohamed Alloghani

Publisher: Springer Nature

Published: 2022-04-05

Total Pages: 258

ISBN-13: 3030922456

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This book provides valuable information on effective, state-of-the-art techniques and approaches for governments, students, researchers, practitioners, entrepreneurs and teachers in the field of artificial intelligence (AI). The book explains the data and AI, types and properties of data, the relation between AI algorithms and data, what makes data AI ready, steps of data pre-processing, data quality, data storage and data platforms. Therefore, this book will be interested by AI practitioners, academics, researchers, and lecturers in computer science, artificial intelligence, machine learning and data sciences.

Computational Intelligence for Oncology and Neurological Disorders
Computational Intelligence for Oncology and Neurological Disorders

Author: Mrutyunjaya Panda

Publisher: CRC Press

Published: 2024-07-15

Total Pages: 292

ISBN-13: 1040085628

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With the advent of computational intelligence-based approaches, such as bio-inspired techniques, and the availability of clinical data from various complex experiments, medical consultants, researchers, neurologists, and oncologists, there is huge scope for CI-based applications in medical oncology and neurological disorders. This book focuses on interdisciplinary research in this field, bringing together medical practitioners dealing with neurological disorders and medical oncology along with CI investigators. The book collects high-quality original contributions, containing the latest developments or applications of practical use and value, presenting interdisciplinary research and review articles in the field of intelligent systems for computational oncology and neurological disorders. Drawing from work across computer science, physics, mathematics, medical science, psychology, cognitive science, oncology, and neurobiology among others, it combines theoretical, applied, computational, experimental, and clinical research. It will be of great interest to any neurology or oncology researchers focused on computational approaches.

Clinical Text Mining
Clinical Text Mining

Author: Hercules Dalianis

Publisher: Springer

Published: 2018-05-14

Total Pages: 192

ISBN-13: 3319785036

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This open access book describes the results of natural language processing and machine learning methods applied to clinical text from electronic patient records. It is divided into twelve chapters. Chapters 1-4 discuss the history and background of the original paper-based patient records, their purpose, and how they are written and structured. These initial chapters do not require any technical or medical background knowledge. The remaining eight chapters are more technical in nature and describe various medical classifications and terminologies such as ICD diagnosis codes, SNOMED CT, MeSH, UMLS, and ATC. Chapters 5-10 cover basic tools for natural language processing and information retrieval, and how to apply them to clinical text. The difference between rule-based and machine learning-based methods, as well as between supervised and unsupervised machine learning methods, are also explained. Next, ethical concerns regarding the use of sensitive patient records for research purposes are discussed, including methods for de-identifying electronic patient records and safely storing patient records. The book’s closing chapters present a number of applications in clinical text mining and summarise the lessons learned from the previous chapters. The book provides a comprehensive overview of technical issues arising in clinical text mining, and offers a valuable guide for advanced students in health informatics, computational linguistics, and information retrieval, and for researchers entering these fields.

Natural Language Processing and Information Systems
Natural Language Processing and Information Systems

Author: Elisabeth Métais

Publisher: Springer

Published: 2013-06-06

Total Pages: 439

ISBN-13: 3642388248

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This book constitutes the refereed proceedings of the 18th International Conference on Applications of Natural Language to Information Systems, held in Salford, UK, in June 2013. The 21 long papers, 15 short papers and 17 poster papers presented in this volume were carefully reviewed and selected from 80 submissions. The papers cover the following topics: requirements engineering, question answering systems, named entity recognition, sentiment analysis and mining, forensic computing, semantic web, and information search.

Artificial Intelligence and Data Science in Environmental Sensing
Artificial Intelligence and Data Science in Environmental Sensing

Author: Mohsen Asadnia

Publisher: Academic Press

Published: 2022-02-09

Total Pages: 326

ISBN-13: 0323905072

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Artificial Intelligence and Data Science in Environmental Sensing provides state-of-the-art information on the inexpensive mass-produced sensors that are used as inputs to artificial intelligence systems. The book discusses the advances of AI and Machine Learning technologies in material design for environmental areas. It is an excellent resource for researchers and professionals who work in the field of data processing, artificial intelligence sensors and environmental applications. - Presents tools, connections and proactive solutions to take sustainability programs to the next level - Offers a practical guide for making students proficient in modern electronic data analysis and graphics - Provides knowledge and background to develop specific platforms related to environmental sensing, including control water, air and soil quality, water and wastewater treatment, desalination, pollution mitigation/control, and resource management and recovery