Predictive Analytics of Psychological Disorders in Healthcare

Predictive Analytics of Psychological Disorders in Healthcare

Author: Mamta Mittal

Publisher: Springer Nature

Published: 2022-05-20

Total Pages: 310

ISBN-13: 9811917248

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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.


Book Synopsis Predictive Analytics of Psychological Disorders in Healthcare by : Mamta Mittal

Download or read book Predictive Analytics of Psychological Disorders in Healthcare written by Mamta Mittal and published by Springer Nature. This book was released on 2022-05-20 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Personalized Psychiatry

Personalized Psychiatry

Author: Ives Cavalcante Passos

Publisher: Springer

Published: 2019-02-12

Total Pages: 180

ISBN-13: 3030035530

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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.


Book Synopsis Personalized Psychiatry by : Ives Cavalcante Passos

Download or read book Personalized Psychiatry written by Ives Cavalcante Passos and published by Springer. This book was released on 2019-02-12 with total page 180 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Personalized Psychiatry

Personalized Psychiatry

Author: Flávio Kapczinski

Publisher:

Published: 2019

Total Pages:

ISBN-13: 9783030035549

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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. .


Book Synopsis Personalized Psychiatry by : Flávio Kapczinski

Download or read book Personalized Psychiatry written by Flávio Kapczinski and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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. .

Artificial Intelligence in Behavioral and Mental Health Care

Artificial Intelligence in Behavioral and Mental Health Care

Author: David D. Luxton

Publisher: Academic Press

Published: 2015-09-10

Total Pages: 308

ISBN-13: 0128007923

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Artificial Intelligence in Behavioral and Mental Health Care summarizes recent advances in artificial intelligence as it applies to mental health clinical practice. Each chapter provides a technical description of the advance, review of application in clinical practice, and empirical data on clinical efficacy. In addition, each chapter includes a discussion of practical issues in clinical settings, ethical considerations, and limitations of use. The book encompasses AI based advances in decision-making, in assessment and treatment, in providing education to clients, robot assisted task completion, and the use of AI for research and data gathering. This book will be of use to mental health practitioners interested in learning about, or incorporating AI advances into their practice and for researchers interested in a comprehensive review of these advances in one source. Summarizes AI advances for use in mental health practice Includes advances in AI based decision-making and consultation Describes AI applications for assessment and treatment Details AI advances in robots for clinical settings Provides empirical data on clinical efficacy Explores practical issues of use in clinical settings


Book Synopsis Artificial Intelligence in Behavioral and Mental Health Care by : David D. Luxton

Download or read book Artificial Intelligence in Behavioral and Mental Health Care written by David D. Luxton and published by Academic Press. This book was released on 2015-09-10 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence in Behavioral and Mental Health Care summarizes recent advances in artificial intelligence as it applies to mental health clinical practice. Each chapter provides a technical description of the advance, review of application in clinical practice, and empirical data on clinical efficacy. In addition, each chapter includes a discussion of practical issues in clinical settings, ethical considerations, and limitations of use. The book encompasses AI based advances in decision-making, in assessment and treatment, in providing education to clients, robot assisted task completion, and the use of AI for research and data gathering. This book will be of use to mental health practitioners interested in learning about, or incorporating AI advances into their practice and for researchers interested in a comprehensive review of these advances in one source. Summarizes AI advances for use in mental health practice Includes advances in AI based decision-making and consultation Describes AI applications for assessment and treatment Details AI advances in robots for clinical settings Provides empirical data on clinical efficacy Explores practical issues of use in clinical settings

Combating Women's Health Issues with Machine Learning

Combating Women's Health Issues with Machine Learning

Author: D. Jude Hemanth

Publisher: CRC Press

Published: 2023-10-23

Total Pages: 251

ISBN-13: 100096468X

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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.


Book Synopsis Combating Women's Health Issues with Machine Learning by : D. Jude Hemanth

Download or read book Combating Women's Health Issues with Machine Learning written by D. Jude Hemanth and published by CRC Press. This book was released on 2023-10-23 with total page 251 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Big data analytics for smart healthcare applications

Big data analytics for smart healthcare applications

Author: Celestine Iwendi

Publisher: Frontiers Media SA

Published: 2023-04-17

Total Pages: 1365

ISBN-13: 2832515754

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Book Synopsis Big data analytics for smart healthcare applications by : Celestine Iwendi

Download or read book Big data analytics for smart healthcare applications written by Celestine Iwendi and published by Frontiers Media SA. This book was released on 2023-04-17 with total page 1365 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Mental Health Informatics

Mental Health Informatics

Author: Jessica D. Tenenbaum

Publisher: Springer Nature

Published: 2021-11-18

Total Pages: 540

ISBN-13: 3030705587

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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.


Book Synopsis Mental Health Informatics by : Jessica D. Tenenbaum

Download or read book Mental Health Informatics written by Jessica D. Tenenbaum and published by Springer Nature. This book was released on 2021-11-18 with total page 540 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Patient-Centric Analytics in Health Care

Patient-Centric Analytics in Health Care

Author: Gregory J. Privitera

Publisher: Lexington Books

Published: 2017-12-13

Total Pages: 217

ISBN-13: 1498550983

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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.


Book Synopsis Patient-Centric Analytics in Health Care by : Gregory J. Privitera

Download or read book Patient-Centric Analytics in Health Care written by Gregory J. Privitera and published by Lexington Books. This book was released on 2017-12-13 with total page 217 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Microbial Metagenomics in Effluent Treatment Plant

Microbial Metagenomics in Effluent Treatment Plant

Author: Maulin P. Shah

Publisher: Elsevier

Published: 2024-05-24

Total Pages: 290

ISBN-13: 0443135320

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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


Book Synopsis Microbial Metagenomics in Effluent Treatment Plant by : Maulin P. Shah

Download or read book Microbial Metagenomics in Effluent Treatment Plant written by Maulin P. Shah and published by Elsevier. This book was released on 2024-05-24 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Computational Techniques in Neuroscience

Computational Techniques in Neuroscience

Author: Kamal Malik

Publisher: CRC Press

Published: 2023-11-14

Total Pages: 243

ISBN-13: 1000994147

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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.


Book Synopsis Computational Techniques in Neuroscience by : Kamal Malik

Download or read book Computational Techniques in Neuroscience written by Kamal Malik and published by CRC Press. This book was released on 2023-11-14 with total page 243 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.