Leveraging Biomedical And Healthcare Data

Book Description :

Leveraging Biomedical and Healthcare Data: Semantics, Analytics and Knowledge provides an overview of the approaches used in semantic systems biology, introduces novel areas of its application, and describes step-wise protocols for transforming heterogeneous data into useful knowledge that can influence healthcare and biomedical research. Given the astronomical increase in the number of published reports, papers, and datasets over the last few decades, the ability to curate this data has become a new field of biomedical and healthcare research. This book discusses big data text-based mining to better understand the molecular architecture of diseases and to guide health care decision. It will be a valuable resource for bioinformaticians and members of several areas of the biomedical field who are interested in understanding more about how to process and apply great amounts of data to improve their research. Includes at each section resource pages containing a list of available curated raw and processed data that can be used by researchers in the field Provides demonstrative and relevant examples that serve as a general tutorial Presents a list of algorithm names and computational tools available for basic and clinical researchers

Book Description :

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.

Book Description :

On February 26, 2020, the Board on Health Sciences Policy of the National Academies of Sciences, Engineering, and Medicine hosted a 1-day public workshop in Washington, DC, to examine current and emerging bioethical issues that might arise in the context of biomedical research and to consider research topics in bioethics that could benefit from further attention. The scope of bioethical issues in research is broad, but this workshop focused on issues related to the development and use of digital technologies, artificial intelligence, and machine learning in research and clinical practice; issues emerging as nontraditional approaches to health research become more widespread; the role of bioethics in addressing racial and structural inequalities in health; and enhancing the capacity and diversity of the bioethics workforce. This publication summarizes the presentations and discussions from the workshop.

Book Description :

In 2019 the world was struck with the Coronavirus (COVID-19) infecting major portions of the world’s population. There were no vaccines or treatments available to help mitigate the disease or offer a cure. The world's health systems were inundated with massive numbers of patients with varying ranges of symptoms, acuity, and levels of criticality. The world's healthcare organizations soon found themselves in an unmanageable situation, directly impacting the ability to manage patients across the entire healthcare environment. Most healthcare institutions had plans for emergency preparedness and procedures to deal with temporary crises, none of which were effective against the impact of COVID-19. COVID-19 was a highly contagious disease, resulting in high volumes of admissions with long lengths of stay. The virus quickly overwhelmed institutions with large patient volumes, resulting in shortages of patient beds, medical equipment, personal protective devices, cleaning agents, and other critical supplies. Hospital operations were further impacted by staff shortages due to exposure, resulting contagion, the shutdown of transit systems, and responsibilities at home due to school and business closures. This timely and important book describes the impact on the hospital ability to provide patient care and how healthcare institutions leveraged diverse technology solutions to combat the impact of COVID-19 on providing patient care. The authors also discuss implementation of these technology solutions and the many lessons learned of how healthcare institutions can enhance their emergency preparedness in the future from the COVID experience. The authors would like to acknowledge, thank, and dedicate this book to the hundreds of thousands of healthcare workers around the world who spent countless hours and put their own lives and families lives at risk to help patients though this pandemic.

Book Description :

The International Conference on Communication and Computing Systems (ICCCS 2018) provides a high-level international forum for researchers and recent advances in the field of electronic devices, computing, big data analytics, cyber security, quantum computing, biocomputing, telecommunication, etc. The aim of the conference was to bridge the gap between the technological advancements in the industry and the academic research.

Book Description :

BIG DATA ANALYTICS AND MACHINE INTELLIGENCE IN BIOMEDICAL AND HEALTH INFORMATICS Provides coverage of developments and state-of-the-art methods in the broad and diversified data analytics field and applicable areas such as big data analytics, data mining, and machine intelligence in biomedical and health informatics. The novel applications of Big Data Analytics and machine intelligence in the biomedical and healthcare sector is an emerging field comprising computer science, medicine, biology, natural environmental engineering, and pattern recognition. Biomedical and health informatics is a new era that brings tremendous opportunities and challenges due to the plentifully available biomedical data and the aim is to ensure high-quality and efficient healthcare by analyzing the data. The 12 chapters in??Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics??cover the latest advances and developments in health informatics, data mining, machine learning, and artificial intelligence. They have been organized with respect to the similarity of topics addressed, ranging from issues pertaining to the Internet of Things (IoT) for biomedical engineering and health informatics, computational intelligence for medical data processing, and Internet of Medical Things??(IoMT). New researchers and practitioners working in the field will benefit from reading the book as they can quickly ascertain the best performing methods and compare the different approaches. Audience Researchers and practitioners working in the fields of biomedicine, health informatics, big data analytics, Internet of Things, and machine learning.

Book Description :

This book seeks to promote the exploitation of data science in healthcare systems. The focus is on advancing the automated analytical methods used to extract new knowledge from data for healthcare applications. To do so, the book draws on several interrelated disciplines, including machine learning, big data analytics, statistics, pattern recognition, computer vision, and Semantic Web technologies, and focuses on their direct application to healthcare. Building on three tutorial-like chapters on data science in healthcare, the following eleven chapters highlight success stories on the application of data science in healthcare, where data science and artificial intelligence technologies have proven to be very promising. This book is primarily intended for data scientists involved in the healthcare or medical sector. By reading this book, they will gain essential insights into the modern data science technologies needed to advance innovation for both healthcare businesses and patients. A basic grasp of data science is recommended in order to fully benefit from this book.

Book Description :

This volume constitutes the refereed proceedings of the 8th Workshop on Engineering Applications, WEA 2021, held in Medellín, Colombia, in October 2021. Due to the COVID-19 pandemic the conference was held in a hybrid mode. The 33 revised full papers and 11 short papers presented in this volume were carefully reviewed and selected from 127 submissions. The papers are organized in the following topical sections: computational intelligence; bioengineering; Internet of Things (IoT); optimization and operations research; engineering applications.

Book Description :

Mild traumatic brain injury (mTBI), directly related to chronic traumatic encephalopathy, presents a crisis in contact sports, the military, and public health. Mild Traumatic Brain Injury: A Science and Engineering Perspective reviews current understanding of mTBI, methods of diagnosis, treatment, policy concerns, and emerging technologies. It details the neurophysiology and epidemiology of brain injuries by presenting disease models and descriptions of nucleating events, characterizes sensors, imagers, and related diagnostic measures used for evaluating and identifying brain injuries, and relates emerging bioinformatics analysis with mTBI markers. The book goes on to discuss issues with sports medicine and military issues; covers therapeutic strategies, surgeries, and future developments; and finally addresses drug trials and candidates for therapy. The broad coverage and accessible discussions will appeal to professionals in diverse fields related to mTBI, students of neurology, medicine, and biology, as well as policy makers and lay persons interested in this hot topic. Features Summarizes the entire scope of the field of mTBI Details the neurophysiology, epidemiology, and presents disease models and descriptions of nucleating events Characterizes sensors, imagers, and related diagnostic measures and relates emerging bioinformatics analysis with mTBI markers Discusses issues with sports medicine and military issues Covers therapeutic strategies, surgeries, and future developments and addresses drug trials and candidates Dr Mark Mentzer earned his PhD in Electrical Engineering from the University of Delaware. He is a former research scientist at the US Army Research Laboratory where he studied mild traumatic brain injury and developed early-detection brain injury helmet sensors. He is a certified test director and contracting officer representative. He possesses two Level-III Defense Acquisition University Certifications in Science and Technology Management and in Test and Evaluation. During his career, he developed a wide range of sensors and instrumentation as well as biochemical processes to assess brain trauma. Mentzer currently teaches graduate systems engineering and computer science courses at the University of Maryland University College.

Book Description :

Since 1992 the Department of Defense (DOD), through the U.S. Army Medical Research and Material Command, has received congressionally earmarked appropriations for programs of biomedical research on prostate, breast, and ovarian cancer; neurofibromatosis; tuberous sclerosis; and other health problems. Appropriations for these Congressionally Directed Medical Research Programs are used to support peer reviewed extramural research project, training, and infrastructure grants. Congress has become concerned about funding increases for these programs given current demands on the military budget. At the request of Congress, the Institute of Medicine (IOM) examined possibilities of augmenting program funding from alternative sources. The resulting IOM book, Strategies to Leverage Research Funding: Guiding DOD's Peer Reviewed Medical Research Programs, focuses on nonfederal and private sector contributions that could extend the appropriated funds without biasing the peer review project selection process.

Book Description :

Datasets in the machine learning for health and biomedicine domain are often noisy, irregularly sampled, only sparsely labeled, and small relative to the dimensionality of the both the data and the tasks. These problems motivate the use of representation learning in this domain, which encompasses a variety of techniques designed to produce representations of a dataset that are amenable to downstream modelling tasks. Representation learning in this domain can also take advantage of the significant external knowledge in the biomedical domain. In this thesis, I will explore novel pre-training and representation learning strategies for biomedical data which leverage external structure or knowledge to inform learning at both local and global scales. These techniques will be explored in 4 chapters: (1) leveraging unlabeled data to infer distributional constraints in a semi-supervised learning setting; (2) using graph convolutional neural networks over gene-gene co-regulatory networks to improve modelling of gene expression data; (3) adapting pre-training techniques from natural language processing to electronic health record data, and showing that novel methods are needed for electronic health record timeseries data; and (4) asserting global structure in pre-training applications through structure-inducing pre-training.

Book Description :

Representation Learning is ubiquitous in state-of-the-art machine learning workflow, including data exploration/visualization, data preprocessing, data model learning, and model interpretations. However, the majority of the newly proposed Representation Learning methods are more suitable for problems with a large amount of data. Applying these methods to problems with a limited amount of data may lead to unsatisfactory performance. Therefore, there is a need for developing Representation Learning methods which are tailored for problems with ``small data", such as, clinical and biomedical data analytics. In this dissertation, we describe our studies of tackling the challenging clinical and biomedical data analytics problem from four perspectives: data preprocessing, temporal data representation learning, output representation learning, and joint input-output representation learning. Data scaling is an important component in data preprocessing. The objective in data scaling is to scale/transform the raw features into reasonable ranges such that each feature of an instance will be equally exploited by the machine learning model. For example, in a credit flaw detection task, a machine learning model may utilize a person's credit score and annual income as features, but because the ranges of these two features are different, a machine learning model may consider one more heavily than another. In this dissertation, I thoroughly introduce the problem in data scaling and describe an approach for data scaling which can intrinsically handle the outlier problem and lead to better model prediction performance. Learning new representations for data in the unstandardized form is a common task in data analytics and data science applications. Usually, data come in a tubular form, namely, the data is represented by a table in which each row is a feature (row) vector of an instance. However, it is also common that the data are not in this form; for example, texts, images, and video/audio records. In this dissertation, I describe the challenge of analyzing imperfect multivariate time series data in healthcare and biomedical research and show that the proposed method can learn a powerful representation to encounter various imperfections and lead to an improvement of prediction performance. Learning output representations is a new aspect of Representation Learning, and its applications have shown promising results in complex tasks, including computer vision and recommendation systems. The main objective of an output representation algorithm is to explore the relationship among the target variables, such that a prediction model can efficiently exploit the similarities and potentially improve prediction performance. In this dissertation, I describe a learning framework which incorporates output representation learning to time-to-event estimation. Particularly, the approach learns the model parameters and time vectors simultaneously. Experimental results do not only show the effectiveness of this approach but also show the interpretability of this approach from the visualizations of the time vectors in 2-D space. Learning the input (feature) representation, output representation, and predictive modeling are closely related to each other. Therefore, it is a very natural extension of the state-of-the-art by considering them together in a joint framework. In this dissertation, I describe a large-margin ranking-based learning framework for time-to-event estimation with joint input embedding learning, output embedding learning, and model parameter learning. In the framework, I cast the functional learning problem to a kernel learning problem, and by adopting the theories in Multiple Kernel Learning, I propose an efficient optimization algorithm. Empirical results also show its effectiveness on several benchmark datasets.

Book Description :

Within the healthcare domain, big data is defined as any ``high volume, high diversity biological, clinical, environmental, and lifestyle information collected from single individuals to large cohorts, in relation to their health and wellness status, at one or several time points.'' Such data is crucial because within it lies vast amounts of invaluable information that could potentially change a patient's life, opening doors to alternate therapies, drugs, and diagnostic tools. Signal Processing and Machine Learning for Biomedical Big Data thus discusses modalities; the numerous ways in which this data is captured via sensors; and various sample rates and dimensionalities. Capturing, analyzing, storing, and visualizing such massive data has required new shifts in signal processing paradigms and new ways of combining signal processing with machine learning tools. This book covers several of these aspects in two ways: firstly, through theoretical signal processing chapters where tools aimed at big data (be it biomedical or otherwise) are described; and, secondly, through application-driven chapters focusing on existing applications of signal processing and machine learning for big biomedical data. This text aimed at the curious researcher working in the field, as well as undergraduate and graduate students eager to learn how signal processing can help with big data analysis. It is the hope of Drs. Sejdic and Falk that this book will bring together signal processing and machine learning researchers to unlock existing bottlenecks within the healthcare field, thereby improving patient quality-of-life. Provides an overview of recent state-of-the-art signal processing and machine learning algorithms for biomedical big data, including applications in the neuroimaging, cardiac, retinal, genomic, sleep, patient outcome prediction, critical care, and rehabilitation domains. Provides contributed chapters from world leaders in the fields of big data and signal processing, covering topics such as data quality, data compression, statistical and graph signal processing techniques, and deep learning and their applications within the biomedical sphere. This book’s material covers how expert domain knowledge can be used to advance signal processing and machine learning for biomedical big data applications.

Book Description :

Longitudinal data collection and analysis are critical to social, demographic, and health research, policy, and practice. They are regularly used to address questions of demographic and health trends, policy and program evaluation, and causality. Panel studies, cohort studies, and longitudinal community studies have proved particularly important in developing countries that lack vital registration systems and comprehensive sources of information on the demographic and health situation of their populations. Research using data from such studies has led to scientific advances and improvements in the well-being of individuals in developing countries. Yet questions remain about the usefulness of these studies relative to their expense (and relative to cross-sectional surveys) and about the appropriate choice of alternative longitudinal strategies in different contexts. For these reasons, the Committee on Population convened a workshop to examine the comparative strengths and weaknesses of various longitudinal approaches in addressing demographic and health questions in developing countries and to consider ways to strengthen longitudinal data collection and analysis. This report summarizes the discussion and opinions voiced at that workshop.

Book Description :

The latest developments in data, informatics and technology continue to enable health professionals and informaticians to improve healthcare for the benefit of patients everywhere. This book presents full papers from ICIMTH 2019, the 17th International Conference on Informatics, Management and Technology in Healthcare, held in Athens, Greece from 5 to 7 July 2019. Of the 150 submissions received, 95 were selected for presentation at the conference following review and are included here. The conference focused on increasing and improving knowledge of healthcare applications spanning the entire spectrum from clinical and health informatics to public health informatics as applied in the healthcare domain. The field of biomedical and health informatics is examined in a very broad framework, presenting the research and application outcomes of informatics from cell to population and exploring a number of technologies such as imaging, sensors, and biomedical equipment, together with management and organizational aspects including legal and social issues. Setting research priorities in health informatics is also addressed. Providing an overview of the latest developments in health informatics, the book will be of interest to all those working in the field.

Book Description :

Download Innovations and Applications of AI IoT and Cognitive Technologies book written by Jingyuan Zhao,V. Vinoth Kumar, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Book Description :

Smart mobile systems, eHealth and telemedicine, as well as social media and gamification, have all become important enablers for the provision of the next generation of health services. This book presents the proceedings of the 13th International Conference on Wearable, Micro and Nano Technologies for Personalised Health (pHealth 2016), held in Heraklion, Crete, in May 2016. pHealth 2016 brings together experts from medical, technological, political, administrative, legal and social domains with the aim of further emphasizing the integration of biology and medical data, systems and information using mobile technologies. The book includes two keynotes and two specially invited talks as well as 21 oral and 10 poster presentations selected by a rigorous review process (with a rejection rate of more than 30%) from the more than 45 submissions to the conference. The book is divided into two sections. The first covers mHealth, devices, applications and biosensors and the second deals with smart personal health systems, deep learning, interoperability and precision medicine. Subjects covered include the development of micro-, nano-, bio- and smart-systems with an emphasis on personalized health, virtual care, precision medicine, big bio data management and analytics, as well as security, privacy and safety issues. This book will be of interest to all those whose work involves the provision of healthcare, both today and into the future.

Book Description :

Successful digital healthcare depends on the effective flow of a complete chain of information; from the sensor, via multiple steps of processing, to the actuator, which can be anything from a human healthcare professional to a robot. Along this pathway, methods for automating the processing of information, like signal processing, machine learning, predictive analytics and decision support, play an increasing role in providing actionable information and supporting personalized and preventive healthcare concepts in both biomedical and digital healthcare systems and applications. ICT systems in healthcare and biomedical systems and devices are very closely related, and in the future they will become increasingly intertwined. Indeed, it is already often difficult to delineate where the one ends and the other begins. This book presents the intended proceedings of the dHealth 2020 annual conference on the general topic of health Informatics and digital health, which was due to be held in Vienna, Austria, on 19 and 20 May 2020, but which was cancelled due to the COVID-19 pandemic. The decision was nevertheless taken to publish these proceedings, which include the 40 papers which would have been delivered at the conference. The special topic for the 2020 edition of the conference was Biomedical Informatics for Health and Care. The book provides an overview of current developments in health informatics and digital health, and will be of interest to researchers and healthcare practitioners alike.

Book Description :

Leveraging Artificial Intelligence in Global Epidemics provides readers with a detailed technical description of the role Artificial Intelligence plays in various stages of a disease outbreak, using COVID-19 as a case study. In the fight against epidemics, medical staff are on the front line; but behind the lines the battle is fought by researchers, and data scientists. Artificial Intelligence has been helping researchers with computer modeling and simulation for predictions about disease progression, the overall economic situation, tax incomes and population development. In the same manner, AI can prepare researchers for any emergency situation by backing the medical science. Artificial Intelligence plays a key and cutting-edge role in the preparedness for and dealing with the outbreak of global epidemics. It can help researchers analyze global data about known viruses to predict the patterns of the next pandemic and the impacts it will have. Not only prediction, AI plays an increasingly important role in assessing readiness, early detection, identification of patients, generating recommendations, situation awareness and more. It is up to the right input and the innovative ways by humans to leverage what AI can do. As COVID-19 has grabbed the world and its economy today, an analysis of the COVID-19 outbreak and the global responses and analytics will pay a long way in preparing humanity for such future situations. Provides readers with understanding of how Artificial Intelligence can be applied to the prediction, forecasting, detection, and testing of global epidemics, using COVID-19 and other recent epidemics such as Ebola, Corona viruses, Zika, influenza, Dengue, Chikungaya, and malaria as case studies Includes background material regarding readiness for coping with epidemics, including Machine Learning models for prediction of epidemic outbreaks based on existing data Includes technical coverage of key topics such as generating recommendations to combat outbreaks, genome sequencing, AI-assisted testing, AI-assisted contact tracing, situation awareness and combating disinformation, and the role of Artificial Intelligence and Machine Learning in drug discovery, vaccine development, and drug re-purposing

Book Description :

Learn how information technology intersects with today’s health care! Health Informatics: An Interprofessional Approach, 3rd Edition, follows the tradition of expert informatics educators Ramona Nelson and Nancy Staggers with new lead author, Lynda R. Hardy, to prepare you for success in today’s technology-filled healthcare practice. Concise coverage includes information systems and applications, such as electronic health records, clinical decision support, telehealth, mHealth, ePatients, and social media tools, as well as system implementation. New to this edition are topics that include analytical approaches to health informatics, increased information on FHIR and SMART on FHIR, and the use of health informatics in pandemics. Chapters written by experts in the field provide the most current and accurate information on continually evolving subjects like evidence-based practice, EHRs, PHRs, mobile health, disaster recovery, and simulation. Objectives, key terms, and an abstract at the beginning of each chapter provide an overview of what each chapter will cover. Case studies and discussion questions at the end of each chapter encourage higher-level thinking that can be applied to real world experiences. Conclusion and Future Directions discussion at the end of each chapter reinforces topics and expands on how the topic will continue to evolve. Open-ended discussion questions at the end of each chapter enhance students’ understanding of the subject covered. mHealth chapter discusses all relevant aspects of mobile health, including global growth, new opportunities in underserved areas, governmental regulations on issues such as data leaking and mining, implications of patient-generated data, legal aspects of provider monitoring of patient-generated data, and increased responsibility by patients. Important content, including FDA- and state-based regulations, project management, big data, and governance models, prepares students for one of nursing’s key specialty areas. UPDATED! Chapters reflect the current and evolving practice of health informatics, using real-life healthcare examples to show how informatics applies to a wide range of topics and issues. NEW! Strategies to promote healthcare equality by freeing algorithms and decision-making from implicit and explicit bias are integrated where applicable. NEW! The latest AACN domains are incorporated throughout to support BSN, Master’s, and DNP programs. NEW! Greater emphasis on the digital patient and the partnerships involved, including decision-making.