The use of Electronic Health Records (EHR)/Electronic Medical Records (EMR) data is becoming more prevalent for research. However, analysis of this type of data has many unique complications due to how they are collected, processed and types of questions that can be answered. This book covers many important topics related to using EHR/EMR data for research including data extraction, cleaning, processing, analysis, inference, and predictions based on many years of practical experience of the authors. The book carefully evaluates and compares the standard statistical models and approaches with those of machine learning and deep learning methods and reports the unbiased comparison results for these methods in predicting clinical outcomes based on the EHR data. Key Features: Written based on hands-on experience of contributors from multidisciplinary EHR research projects, which include methods and approaches from statistics, computing, informatics, data science and clinical/epidemiological domains. Documents the detailed experience on EHR data extraction, cleaning and preparation Provides a broad view of statistical approaches and machine learning prediction models to deal with the challenges and limitations of EHR data. Considers the complete cycle of EHR data analysis.The use of EHR/EMR analysis requires close collaborations between statisticians, informaticians, data scientists and clinical/epidemiological investigators. This book reflects that multidisciplinary perspective.

From the Foreword:"[This book] provides a comprehensive overview of the fundamental concepts in healthcare process management as well as some advanced topics in the cutting-edge research of the closely related areas. This book is ideal for graduate students and practitioners who want to build the foundations and develop novel contributions in healthcare process modeling and management."--Christopher Yang, Drexel UniversityProcess modeling and process management are traversal disciplines which have earned more and more relevance over the last two decades. Several research areas are involved within these disciplines, including database systems, database management, information systems, ERP, operations research, formal languages, and logic. Process Modeling and Management for Healthcare provides the reader with an in-depth analysis of what process modeling and process management techniques can do in healthcare, the major challenges faced, and those challenges remaining to be faced. The book features contributions from leading authors in the field.The book is structured into two parts. Part one covers fundamentals and basic concepts in healthcare. It explores the architecture of a process management environment, the flexibility of a process model, and the compliance of a process model. It also features a real application domain of patients suffering from age-related macular degeneration.Part two of the book includes advanced topics from the leading frontiers of scientific research on process management and healthcare. This section of the book covers software metrics to measure features of the process model as a software artifact. It includes process analysis to discover the formal properties of the process model prior to deploying it in real application domains. Abnormal situations and exceptions, as well as temporal clinical guidelines, are also presented in depth Pro.

The use of Electronic Health Records (EHR)/Electronic Medical Records (EMR) data is becoming more prevalent for research. However, analysis of this type of data has many unique complications due to how they are collected, processed and types of questions that can be answered. This book covers many important topics related to using EHR/EMR data for research including data extraction, cleaning, processing, analysis, inference, and predictions based on many years of practical experience of the authors. The book carefully evaluates and compares the standard statistical models and approaches with those of machine learning and deep learning methods and reports the unbiased comparison results for these methods in predicting clinical outcomes based on the EHR data. Key Features: Written based on hands-on experience of contributors from multidisciplinary EHR research projects, which include methods and approaches from statistics, computing, informatics, data science and clinical/epidemiological domains. Documents the detailed experience on EHR data extraction, cleaning and preparation Provides a broad view of statistical approaches and machine learning prediction models to deal with the challenges and limitations of EHR data. Considers the complete cycle of EHR data analysis.The use of EHR/EMR analysis requires close collaborations between statisticians, informaticians, data scientists and clinical/epidemiological investigators. This book reflects that multidisciplinary perspective.

"An engaging introduction to an exciting multidisciplinary field where positive impact depends less on technology than on understanding and responding to human motivations, specific information needs, and life constraints."-- Betsy L. Humphreys, former Deputy Director, National Library of MedicineThis is a book for people who want to design or promote information technology that helps people be more active and informed participants in their healthcare. Topics include patient portals, wearable devices, apps, websites, smart homes, and online communities focused on health.Consumer Healthcare Informatics: Enabling Digital Health for Everyone educates readers in the core concepts of consumer health informatics: participatory healthcare; health and e-health literacy; user-centered design; information retrieval and trusted information resources; and the ethical dimensions of health information and communication technologies. It presents the current state of knowledge and recent developments in the field of consumer health informatics. The discussions address tailoring information to key user groups, including patients, consumers, caregivers, parents, children and young adults, and older adults. For example, apps are considered as not just a rich consumer technology with the promise of empowered personal data management and connectedness to community and healthcare providers, but also a domain rife with concerns for effectiveness, privacy, and security, requiring both designer and user to engage in critical thinking around their choices.This book’s unique contribution to the field is its focus on the consumer and patient in the context of their everyday life outside the clinical setting. Discussion of tools and technologies is grounded in this perspective and in a context of real-world use and its implications for design. There is an emphasis on empowerment through participatory and people-centered care.

Machine Learning in Medicine covers the state-of-the-art techniques of machine learning and their applications in the medical field. It presents several computer-aided diagnosis (CAD) systems, which have played an important role in the diagnosis of several diseases in the past decade, e.g., cancer detection, resulting in the development of several successful systems.New developments in machine learning may make it possible in the near future to develop machines that are capable of completely performing tasks that currently cannot be completed without human aid, especially in the medical field. This book covers such machines, including convolutional neural networks (CNNs) with different activation functions for small- to medium-size biomedical datasets, detection of abnormal activities stemming from cognitive decline, thermal dose modelling for thermal ablative cancer treatments, dermatological machine learning clinical decision support systems, artificial intelligence-powered ultrasound for diagnosis, practical challenges with possible solutions for machine learning in medical imaging, epilepsy diagnosis from structural MRI, Alzheimer's disease diagnosis, classification of left ventricular hypertrophy, and intelligent medical language understanding.This book will help to advance scientific research within the broad field of machine learning in the medical field. It focuses on major trends and challenges in this area and presents work aimed at identifying new techniques and their use in biomedical analysis, including extensive references at the end of each chapter.

The COVID-19 pandemic upended the lives of many and taught us the critical importance of taking care of one’s health and wellness. Technological advances, coupled with advances in healthcare, has enabled the widespread growth of a new area called mobile health or mHealth that has completely revolutionized how people envision healthcare today. Just as smartphones and tablet computers are rapidly becoming the dominant consumer computer platforms, mHealth technology is emerging as an integral part of consumer health and wellness management regimes. The aim of this book is to inform readers about the this relatively modern technology, from its history and evolution to the current state-of-the-art research developments and the underlying challenges related to privacy and security issues. The book’s intended audience includes individuals interested in learning about mHealth and its contemporary applications, from students to researchers and practitioners working in this field. Both undergraduate and graduate students enrolled in college-level healthcare courses will find this book to be an especially useful companion and will be able to discover and explore novel research directions that will further enrich the field.

Machine Learning in Medicine covers the state-of-the-art techniques of machine learning and their applications in the medical field. It presents several computer-aided diagnosis (CAD) systems, which have played an important role in the diagnosis of several diseases in the past decade, e.g., cancer detection, resulting in the development of several successful systems.New developments in machine learning may make it possible in the near future to develop machines that are capable of completely performing tasks that currently cannot be completed without human aid, especially in the medical field. This book covers such machines, including convolutional neural networks (CNNs) with different activation functions for small- to medium-size biomedical datasets, detection of abnormal activities stemming from cognitive decline, thermal dose modelling for thermal ablative cancer treatments, dermatological machine learning clinical decision support systems, artificial intelligence-powered ultrasound for diagnosis, practical challenges with possible solutions for machine learning in medical imaging, epilepsy diagnosis from structural MRI, Alzheimer's disease diagnosis, classification of left ventricular hypertrophy, and intelligent medical language understanding.This book will help to advance scientific research within the broad field of machine learning in the medical field. It focuses on major trends and challenges in this area and presents work aimed at identifying new techniques and their use in biomedical analysis, including extensive references at the end of each chapter.

From the Foreword:"[This book] provides a comprehensive overview of the fundamental concepts in healthcare process management as well as some advanced topics in the cutting-edge research of the closely related areas. This book is ideal for graduate students and practitioners who want to build the foundations and develop novel contributions in healthcare process modeling and management."--Christopher Yang, Drexel UniversityProcess modeling and process management are traversal disciplines which have earned more and more relevance over the last two decades. Several research areas are involved within these disciplines, including database systems, database management, information systems, ERP, operations research, formal languages, and logic. Process Modeling and Management for Healthcare provides the reader with an in-depth analysis of what process modeling and process management techniques can do in healthcare, the major challenges faced, and those challenges remaining to be faced. The book features contributions from leading authors in the field.The book is structured into two parts. Part one covers fundamentals and basic concepts in healthcare. It explores the architecture of a process management environment, the flexibility of a process model, and the compliance of a process model. It also features a real application domain of patients suffering from age-related macular degeneration.Part two of the book includes advanced topics from the leading frontiers of scientific research on process management and healthcare. This section of the book covers software metrics to measure features of the process model as a software artifact. It includes process analysis to discover the formal properties of the process model prior to deploying it in real application domains. Abnormal situations and exceptions, as well as temporal clinical guidelines, are also presented in depth Pro.

"An engaging introduction to an exciting multidisciplinary field where positive impact depends less on technology than on understanding and responding to human motivations, specific information needs, and life constraints."-- Betsy L. Humphreys, former Deputy Director, National Library of MedicineThis is a book for people who want to design or promote information technology that helps people be more active and informed participants in their healthcare. Topics include patient portals, wearable devices, apps, websites, smart homes, and online communities focused on health.Consumer Healthcare Informatics: Enabling Digital Health for Everyone educates readers in the core concepts of consumer health informatics: participatory healthcare; health and e-health literacy; user-centered design; information retrieval and trusted information resources; and the ethical dimensions of health information and communication technologies. It presents the current state of knowledge and recent developments in the field of consumer health informatics. The discussions address tailoring information to key user groups, including patients, consumers, caregivers, parents, children and young adults, and older adults. For example, apps are considered as not just a rich consumer technology with the promise of empowered personal data management and connectedness to community and healthcare providers, but also a domain rife with concerns for effectiveness, privacy, and security, requiring both designer and user to engage in critical thinking around their choices.This book’s unique contribution to the field is its focus on the consumer and patient in the context of their everyday life outside the clinical setting. Discussion of tools and technologies is grounded in this perspective and in a context of real-world use and its implications for design. There is an emphasis on empowerment through participatory and people-centered care.

The use of Electronic Health Records (EHR)/Electronic Medical Records (EMR) data is becoming more prevalent for research. However, analysis of this type of data has many unique complications due to how they are collected, processed and types of questions that can be answered. This book covers many important topics related to using EHR/EMR data for research including data extraction, cleaning, processing, analysis, inference, and predictions based on many years of practical experience of the authors. The book carefully evaluates and compares the standard statistical models and approaches with those of machine learning and deep learning methods and reports the unbiased comparison results for these methods in predicting clinical outcomes based on the EHR data. Key Features: Written based on hands-on experience of contributors from multidisciplinary EHR research projects, which include methods and approaches from statistics, computing, informatics, data science and clinical/epidemiological domains. Documents the detailed experience on EHR data extraction, cleaning and preparation Provides a broad view of statistical approaches and machine learning prediction models to deal with the challenges and limitations of EHR data. Considers the complete cycle of EHR data analysis.The use of EHR/EMR analysis requires close collaborations between statisticians, informaticians, data scientists and clinical/epidemiological investigators. This book reflects that multidisciplinary perspective.

From the Foreword:"[This book] provides a comprehensive overview of the fundamental concepts in healthcare process management as well as some advanced topics in the cutting-edge research of the closely related areas. This book is ideal for graduate students and practitioners who want to build the foundations and develop novel contributions in healthcare process modeling and management."--Christopher Yang, Drexel UniversityProcess modeling and process management are traversal disciplines which have earned more and more relevance over the last two decades. Several research areas are involved within these disciplines, including database systems, database management, information systems, ERP, operations research, formal languages, and logic. Process Modeling and Management for Healthcare provides the reader with an in-depth analysis of what process modeling and process management techniques can do in healthcare, the major challenges faced, and those challenges remaining to be faced. The book features contributions from leading authors in the field.The book is structured into two parts. Part one covers fundamentals and basic concepts in healthcare. It explores the architecture of a process management environment, the flexibility of a process model, and the compliance of a process model. It also features a real application domain of patients suffering from age-related macular degeneration.Part two of the book includes advanced topics from the leading frontiers of scientific research on process management and healthcare. This section of the book covers software metrics to measure features of the process model as a software artifact. It includes process analysis to discover the formal properties of the process model prior to deploying it in real application domains. Abnormal situations and exceptions, as well as temporal clinical guidelines, are also presented in depth Pro.

The use of Electronic Health Records (EHR)/Electronic Medical Records (EMR) data is becoming more prevalent for research. However, analysis of this type of data has many unique complications due to how they are collected, processed and types of questions that can be answered. This book covers many important topics related to using EHR/EMR data for research including data extraction, cleaning, processing, analysis, inference, and predictions based on many years of practical experience of the authors. The book carefully evaluates and compares the standard statistical models and approaches with those of machine learning and deep learning methods and reports the unbiased comparison results for these methods in predicting clinical outcomes based on the EHR data. Key Features: Written based on hands-on experience of contributors from multidisciplinary EHR research projects, which include methods and approaches from statistics, computing, informatics, data science and clinical/epidemiological domains. Documents the detailed experience on EHR data extraction, cleaning and preparation Provides a broad view of statistical approaches and machine learning prediction models to deal with the challenges and limitations of EHR data. Considers the complete cycle of EHR data analysis.The use of EHR/EMR analysis requires close collaborations between statisticians, informaticians, data scientists and clinical/epidemiological investigators. This book reflects that multidisciplinary perspective.

"An engaging introduction to an exciting multidisciplinary field where positive impact depends less on technology than on understanding and responding to human motivations, specific information needs, and life constraints."-- Betsy L. Humphreys, former Deputy Director, National Library of MedicineThis is a book for people who want to design or promote information technology that helps people be more active and informed participants in their healthcare. Topics include patient portals, wearable devices, apps, websites, smart homes, and online communities focused on health.Consumer Healthcare Informatics: Enabling Digital Health for Everyone educates readers in the core concepts of consumer health informatics: participatory healthcare; health and e-health literacy; user-centered design; information retrieval and trusted information resources; and the ethical dimensions of health information and communication technologies. It presents the current state of knowledge and recent developments in the field of consumer health informatics. The discussions address tailoring information to key user groups, including patients, consumers, caregivers, parents, children and young adults, and older adults. For example, apps are considered as not just a rich consumer technology with the promise of empowered personal data management and connectedness to community and healthcare providers, but also a domain rife with concerns for effectiveness, privacy, and security, requiring both designer and user to engage in critical thinking around their choices.This book’s unique contribution to the field is its focus on the consumer and patient in the context of their everyday life outside the clinical setting. Discussion of tools and technologies is grounded in this perspective and in a context of real-world use and its implications for design. There is an emphasis on empowerment through participatory and people-centered care.

Machine Learning in Medicine covers the state-of-the-art techniques of machine learning and their applications in the medical field. It presents several computer-aided diagnosis (CAD) systems, which have played an important role in the diagnosis of several diseases in the past decade, e.g., cancer detection, resulting in the development of several successful systems.New developments in machine learning may make it possible in the near future to develop machines that are capable of completely performing tasks that currently cannot be completed without human aid, especially in the medical field. This book covers such machines, including convolutional neural networks (CNNs) with different activation functions for small- to medium-size biomedical datasets, detection of abnormal activities stemming from cognitive decline, thermal dose modelling for thermal ablative cancer treatments, dermatological machine learning clinical decision support systems, artificial intelligence-powered ultrasound for diagnosis, practical challenges with possible solutions for machine learning in medical imaging, epilepsy diagnosis from structural MRI, Alzheimer's disease diagnosis, classification of left ventricular hypertrophy, and intelligent medical language understanding.This book will help to advance scientific research within the broad field of machine learning in the medical field. It focuses on major trends and challenges in this area and presents work aimed at identifying new techniques and their use in biomedical analysis, including extensive references at the end of each chapter.

The COVID-19 pandemic upended the lives of many and taught us the critical importance of taking care of one’s health and wellness. Technological advances, coupled with advances in healthcare, has enabled the widespread growth of a new area called mobile health or mHealth that has completely revolutionized how people envision healthcare today. Just as smartphones and tablet computers are rapidly becoming the dominant consumer computer platforms, mHealth technology is emerging as an integral part of consumer health and wellness management regimes. The aim of this book is to inform readers about the this relatively modern technology, from its history and evolution to the current state-of-the-art research developments and the underlying challenges related to privacy and security issues. The book’s intended audience includes individuals interested in learning about mHealth and its contemporary applications, from students to researchers and practitioners working in this field. Both undergraduate and graduate students enrolled in college-level healthcare courses will find this book to be an especially useful companion and will be able to discover and explore novel research directions that will further enrich the field.

Machine Learning in Medicine covers the state-of-the-art techniques of machine learning and their applications in the medical field. It presents several computer-aided diagnosis (CAD) systems, which have played an important role in the diagnosis of several diseases in the past decade, e.g., cancer detection, resulting in the development of several successful systems.New developments in machine learning may make it possible in the near future to develop machines that are capable of completely performing tasks that currently cannot be completed without human aid, especially in the medical field. This book covers such machines, including convolutional neural networks (CNNs) with different activation functions for small- to medium-size biomedical datasets, detection of abnormal activities stemming from cognitive decline, thermal dose modelling for thermal ablative cancer treatments, dermatological machine learning clinical decision support systems, artificial intelligence-powered ultrasound for diagnosis, practical challenges with possible solutions for machine learning in medical imaging, epilepsy diagnosis from structural MRI, Alzheimer's disease diagnosis, classification of left ventricular hypertrophy, and intelligent medical language understanding.This book will help to advance scientific research within the broad field of machine learning in the medical field. It focuses on major trends and challenges in this area and presents work aimed at identifying new techniques and their use in biomedical analysis, including extensive references at the end of each chapter.

From the Foreword:"[This book] provides a comprehensive overview of the fundamental concepts in healthcare process management as well as some advanced topics in the cutting-edge research of the closely related areas. This book is ideal for graduate students and practitioners who want to build the foundations and develop novel contributions in healthcare process modeling and management."--Christopher Yang, Drexel UniversityProcess modeling and process management are traversal disciplines which have earned more and more relevance over the last two decades. Several research areas are involved within these disciplines, including database systems, database management, information systems, ERP, operations research, formal languages, and logic. Process Modeling and Management for Healthcare provides the reader with an in-depth analysis of what process modeling and process management techniques can do in healthcare, the major challenges faced, and those challenges remaining to be faced. The book features contributions from leading authors in the field.The book is structured into two parts. Part one covers fundamentals and basic concepts in healthcare. It explores the architecture of a process management environment, the flexibility of a process model, and the compliance of a process model. It also features a real application domain of patients suffering from age-related macular degeneration.Part two of the book includes advanced topics from the leading frontiers of scientific research on process management and healthcare. This section of the book covers software metrics to measure features of the process model as a software artifact. It includes process analysis to discover the formal properties of the process model prior to deploying it in real application domains. Abnormal situations and exceptions, as well as temporal clinical guidelines, are also presented in depth Pro.

"An engaging introduction to an exciting multidisciplinary field where positive impact depends less on technology than on understanding and responding to human motivations, specific information needs, and life constraints."-- Betsy L. Humphreys, former Deputy Director, National Library of MedicineThis is a book for people who want to design or promote information technology that helps people be more active and informed participants in their healthcare. Topics include patient portals, wearable devices, apps, websites, smart homes, and online communities focused on health.Consumer Healthcare Informatics: Enabling Digital Health for Everyone educates readers in the core concepts of consumer health informatics: participatory healthcare; health and e-health literacy; user-centered design; information retrieval and trusted information resources; and the ethical dimensions of health information and communication technologies. It presents the current state of knowledge and recent developments in the field of consumer health informatics. The discussions address tailoring information to key user groups, including patients, consumers, caregivers, parents, children and young adults, and older adults. For example, apps are considered as not just a rich consumer technology with the promise of empowered personal data management and connectedness to community and healthcare providers, but also a domain rife with concerns for effectiveness, privacy, and security, requiring both designer and user to engage in critical thinking around their choices.This book’s unique contribution to the field is its focus on the consumer and patient in the context of their everyday life outside the clinical setting. Discussion of tools and technologies is grounded in this perspective and in a context of real-world use and its implications for design. There is an emphasis on empowerment through participatory and people-centered care.