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Author: Mamta Mittal
Publisher: Springer Nature
ISBN: 9811917248
Category : Technology & Engineering
Languages : en
Pages : 310
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Book Description
This book discusses an interdisciplinary field which combines two major domains: healthcare and data analytics. It presents research studies by experts helping to fight discontent, distress, anxiety and unrealized potential by using mathematical models, machine learning, artificial intelligence, etc. and take preventive measures beforehand. Psychological disorders and biological abnormalities are significantly related with the applications of cognitive illnesses which has increased significantly in contemporary years and needs rapid investigation. The research content of this book is helpful for psychological undergraduates, health workers and their trainees, therapists, medical psychologists, and nurses.
Author: Mamta Mittal
Publisher: Springer Nature
ISBN: 9811917248
Category : Technology & Engineering
Languages : en
Pages : 310
Get Book
Book Description
This book discusses an interdisciplinary field which combines two major domains: healthcare and data analytics. It presents research studies by experts helping to fight discontent, distress, anxiety and unrealized potential by using mathematical models, machine learning, artificial intelligence, etc. and take preventive measures beforehand. Psychological disorders and biological abnormalities are significantly related with the applications of cognitive illnesses which has increased significantly in contemporary years and needs rapid investigation. The research content of this book is helpful for psychological undergraduates, health workers and their trainees, therapists, medical psychologists, and nurses.
Author: Ives Cavalcante Passos
Publisher: Springer
ISBN: 3030035530
Category : Medical
Languages : en
Pages : 180
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Book Description
This book integrates the concepts of big data analytics into mental health practice and research. Mental disorders represent a public health challenge of staggering proportions. According to the most recent Global Burden of Disease study, psychiatric disorders constitute the leading cause of years lost to disability. The high morbidity and mortality related to these conditions are proportional to the potential for overall health gains if mental disorders can be more effectively diagnosed and treated. In order to fill these gaps, analysis in science, industry, and government seeks to use big data for a variety of problems, including clinical outcomes and diagnosis in psychiatry. Multiple mental healthcare providers and research laboratories are increasingly using large data sets to fulfill their mission. Briefly, big data is characterized by high volume, high velocity, variety and veracity of information, and to be useful it must be analyzed, interpreted, and acted upon. As such, focus has to shift to new analytical tools from the field of machine learning that will be critical for anyone practicing medicine, psychiatry and behavioral sciences in the 21st century. Big data analytics is gaining traction in psychiatric research, being used to provide predictive models for both clinical practice and public health systems. As compared with traditional statistical methods that provide primarily average group-level results, big data analytics allows predictions and stratification of clinical outcomes at an individual subject level. Personalized Psychiatry – Big Data Analytics in Mental Health provides a unique opportunity to showcase innovative solutions tackling complex problems in mental health using big data and machine learning. It represents an interesting platform to work with key opinion leaders to document current achievements, introduce new concepts as well as project the future role of big data and machine learning in mental health.
Author: Flávio Kapczinski
Publisher:
ISBN: 9783030035549
Category : HEALTH & FITNESS
Languages : en
Pages :
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Book Description
This book integrates the concepts of big data analytics into mental health practice and research. Mental disorders represent a public health challenge of staggering proportions. According to the most recent Global Burden of Disease study, psychiatric disorders constitute the leading cause of years lost to disability. The high morbidity and mortality related to these conditions are proportional to the potential for overall health gains if mental disorders can be more effectively diagnosed and treated. In order to fill these gaps, analysis in science, industry, and government seeks to use big data for a variety of problems, including clinical outcomes and diagnosis in psychiatry. Multiple mental healthcare providers and research laboratories are increasingly using large data sets to fulfill their mission. Briefly, big data is characterized by high volume, high velocity, variety and veracity of information, and to be useful it must be analyzed, interpreted, and acted upon. As such, focus has to shift to new analytical tools from the field of machine learning that will be critical for anyone practicing medicine, psychiatry and behavioral sciences in the 21st century. Big data analytics is gaining traction in psychiatric research, being used to provide predictive models for both clinical practice and public health systems. As compared with traditional statistical methods that provide primarily average group-level results, big data analytics allows predictions and stratification of clinical outcomes at an individual subject level. Personalized Psychiatry – Big Data Analytics in Mental Health provides a unique opportunity to showcase innovative solutions tackling complex problems in mental health using big data and machine learning. It represents an interesting platform to work with key opinion leaders to document current achievements, introduce new concepts as well as project the future role of big data and machine learning in mental health. .
Author: D. Jude Hemanth
Publisher: CRC Press
ISBN: 100096468X
Category : Medical
Languages : en
Pages : 251
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Book Description
The main focus of this book is the examination of women’s health issues and the role machine learning can play as a solution to these challenges. This book will illustrate advanced, innovative techniques/frameworks/concepts/machine learning methodologies, enhancing the future healthcare system. Combating Women’s Health Issues with Machine Learning: Challenges and Solutions examines the fundamental concepts and analysis of machine learning algorithms. The editors and authors of this book examine new approaches for different age-related medical issues that women face. Topics range from diagnosing diseases such as breast and ovarian cancer to using deep learning in prenatal ultrasound diagnosis. The authors also examine the best machine learning classifier for constructing the most accurate predictive model for women’s infertility risk. Among the topics discussed are gender differences in type 2 diabetes care and its management as it relates to gender using artificial intelligence. The book also discusses advanced techniques for evaluating and managing cardiovascular disease symptoms, which are more common in women but often overlooked or misdiagnosed by many healthcare providers. The book concludes by presenting future considerations and challenges in the field of women’s health using artificial intelligence. This book is intended for medical researchers, healthcare technicians, scientists, programmers and graduate-level students looking to understand better and develop applications of machine learning/deep learning in healthcare scenarios, especially concerning women’s health conditions.
Author: Celestine Iwendi
Publisher: Frontiers Media SA
ISBN: 2832515754
Category : Medical
Languages : en
Pages : 1365
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Book Description
Author: Jessica D. Tenenbaum
Publisher: Springer Nature
ISBN: 3030705587
Category : Medical
Languages : en
Pages : 540
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Book Description
This textbook provides a detailed resource introducing the subdiscipline of mental health informatics. It systematically reviews the methods, paradigms, tools and knowledge base in both clinical and bioinformatics and across the spectrum from research to clinical care. Key foundational technologies, such as terminologies, ontologies and data exchange standards are presented and given context within the complex landscape of mental health conditions, research and care. The learning health system model is utilized to emphasize the bi-directional nature of the translational science associated with mental health processes. Descriptions of the data, technologies, paradigms and products that are generated by and used in each process and their limitations are discussed. Mental Health Informatics: Enabling a Learning Mental Healthcare System is a comprehensive introductory resource for students, educators and researchers in mental health informatics and related behavioral sciences. It is an ideal resource for use in a survey course for both pre- and post-doctoral training programs, as well as for healthcare administrators, funding entities, vendors and product developers working to make mental healthcare more evidence-based.
Author: Gregory J. Privitera
Publisher: Lexington Books
ISBN: 1498550983
Category : Psychology
Languages : en
Pages : 217
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Book Description
In Patient-Centric Analytics in Health Care: Driving Value in Clinical Settings and Psychological Practice, James J. Gillespie and Gregory J. Privitera introduce a framework that explores the utility of analytics for managing care that is based on six key inputs of the health care system: patients, policy makers, providers, pharmacies, pharmaceuticals, and payers. Understanding the roles of these 6 P’s and the utility of analytics to promote data-driven decision models can lead to new innovations. These improvements can enhance quality, increase access, and reduce costs, and thereby drive value for the most important stakeholders in health care: the patients. As the accessibility and volume of data continues to increase, there is a growing desire to utilize data to guide and optimize decision-making in health care environments. There is a wealth of data in health care organizations and much of it is not fully utilized. In today’s climate, these organizations are under increased regulatory and financial pressures to deliver measurable value, particularly as it relates to the quality of patient care in clinical and diagnostic settings. This book includes short contributions from practitioners, including Laurie Branch, Puneet Chahal, Patrick C. Cunningham, Star* Cunningham, Matthew Dreckmeier, Joseph P. Gaspero, Sherri Matis-Mitchell, Gail Mayeaux, Edwin K. Morris, Plamen Petrov, Steven Press, Andrew J. Privitera, Derek Walton, and Daniel Yunker.
Author: Maulin P. Shah
Publisher: Elsevier
ISBN: 0443135320
Category : Technology & Engineering
Languages : en
Pages : 290
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Book Description
Microbial Metagenomics in Effluent Treatment Plant introduces a metagenomic approach characterizing microbial communities?in industrial wastewater treatment, providing an overall picture of metagenomics, its application, processes, and future prospects in the field of bioremediation. It also discusses culture-dependent methods, culture-independent methods, and?enzymatic methods?used to estimate bacterial diversity to monitor temporal and spatial changes in bacterial communities. In addition, a metagenomic approach will be discussed to characterize the microbial communities in industrial wastewater treatment. Researchers, scientists, professors, and students in environmental engineering, applied microbiology, and water treatment will find Microbial Metagenomics in Effluent Treatment Plant helpful in understanding the importance and role of metagenomics in biogeochemical cycles and degradation and detoxification of environmental pollutants. Presents text rich in information and knowledge of metagenomics Introduces novel and powerful insights into the already existing bioremediation process Serves as an easy-to-understand and centralized resource of information with practical application ideas
Author: Kamal Malik
Publisher: CRC Press
ISBN: 1000994147
Category : Technology & Engineering
Languages : en
Pages : 243
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Book Description
The text discusses the techniques of deep learning and machine learning in the field of neuroscience, engineering approaches to study the brain structure and dynamics, convolutional networks for fast, energy-efficient neuromorphic computing, and reinforcement learning in feedback control. It showcases case studies in neural data analysis. Features: Focuses on neuron modeling, development, and direction of neural circuits to explain perception, behavior, and biologically inspired intelligent agents for decision making Showcases important aspects such as human behavior prediction using smart technologies and understanding the modeling of nervous systems Discusses nature-inspired algorithms such as swarm intelligence, ant colony optimization, and multi-agent systems Presents information-theoretic, control-theoretic, and decision-theoretic approaches in neuroscience. Includes case studies in functional magnetic resonance imaging (fMRI) and neural data analysis This reference text addresses different applications of computational neuro-sciences using artificial intelligence, deep learning, and other machine learning techniques to fine-tune the models, thereby solving the real-life problems prominently. It will further discuss important topics such as neural rehabili-tation, brain-computer interfacing, neural control, neural system analysis, and neurobiologically inspired self-monitoring systems. It will serve as an ideal reference text for graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, information technology, and biomedical engineering.
Author: Gopi Battineni
Publisher: Springer Nature
ISBN: 981996637X
Category : Medical
Languages : en
Pages : 345
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Book Description
This book presents a particular area of interest in computing psychiatry with the modelling of mood and anxiety disorders. It highlights various methods for building these models. Clinical applications are prevalent due to the growth and interaction of these multiple approaches. Besides, it outlines some original predictive and computational modelling ideas for enhancing psychological treatment interventions. Computational psychiatry combines multiple levels and types of computation with different data types to improve mental illness understanding, prediction, and treatment.
