Leveraging Data Science for Global Health

Leveraging Data Science for Global Health
Author: Leo Anthony Celi
Publisher: Springer Nature
Total Pages: 471
Release: 2020-07-31
Genre: Medical
ISBN: 3030479943
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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.

Leveraging Data Science for Global Health

Leveraging Data Science for Global Health
Author: Leo Anthony Celi
Publisher: Springer
Total Pages: 475
Release: 2020-09-18
Genre: Medical
ISBN: 9783030479961
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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.

Leveraging Biomedical and Healthcare Data

Leveraging Biomedical and Healthcare Data
Author: Firas Kobeissy
Publisher: Academic Press
Total Pages: 225
Release: 2018-11-23
Genre: Medical
ISBN: 012809561X
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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

Leveraging Artificial Intelligence in Global Epidemics

Leveraging Artificial Intelligence in Global Epidemics
Author: Le Gruenwald
Publisher: Academic Press
Total Pages: 320
Release: 2021-07-28
Genre: Science
ISBN: 032390002X
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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

Global Cardiac Surgery Capacity Development in Low and Middle Income Countries

Global Cardiac Surgery Capacity Development in Low and Middle Income Countries
Author: Jacques Kpodonu
Publisher: Springer Nature
Total Pages: 541
Release: 2021-11-22
Genre: Medical
ISBN: 3030838641
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This book provides a focused resource on how cardiac surgery capacity can be developed and how it assists in the sustainable development and strengthening of associated health systems. Background is provided on the extent of the problems that are experienced in many nations with suggestions for how suitable frameworks can be developed to improve cardiac healthcare provision. Relevant aspects of governance, financial modelling and disease surveillance are all covered. Guidance is also given on how to found and nurture cardiac surgery curriculum and residency programs. Global Cardiac Surgery Capacity Development in Low and Middle Income Countries provides a practically applicable resource on how to treat cardiac patients with limited resources. It identifies the key challenges and presents strategies on how these can be managed, therefore making it a critical tool for those involved in this field.

Global Health Informatics

Global Health Informatics
Author: Leo Anthony G. Celi
Publisher: MIT Press
Total Pages: 465
Release: 2017-04-21
Genre: Medical
ISBN: 0262533200
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Key concepts, frameworks, examples, and lessons learned in designing and implementing health information and communication technology systems in the developing world. The widespread usage of mobile phones that bring computational power and data to our fingertips has enabled new models for tracking and battling disease. The developing world in particular has become a proving ground for innovation in eHealth (using communication and technology tools in healthcare) and mHealth (using the affordances of mobile technology in eHealth systems). In this book, experts from a variety of disciplines—among them computer science, medicine, public health, policy, and business—discuss key concepts, frameworks, examples, and lessons learned in designing and implementing digital health systems in the developing world. The contributors consider such topics as global health disparities and quality of care; aligning eHealth strategies with government policy; the role of monitoring and evaluation in improving care; databases, patient registries, and electronic health records; the lifecycle of a digital health system project; software project management; privacy and security; and evaluating health technology systems.

Demystifying Big Data and Machine Learning for Healthcare

Demystifying Big Data and Machine Learning for Healthcare
Author: Prashant Natarajan
Publisher: CRC Press
Total Pages: 227
Release: 2017-02-15
Genre: Medical
ISBN: 1315389304
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Healthcare transformation requires us to continually look at new and better ways to manage insights – both within and outside the organization today. Increasingly, the ability to glean and operationalize new insights efficiently as a byproduct of an organization’s day-to-day operations is becoming vital to hospitals and health systems ability to survive and prosper. One of the long-standing challenges in healthcare informatics has been the ability to deal with the sheer variety and volume of disparate healthcare data and the increasing need to derive veracity and value out of it. Demystifying Big Data and Machine Learning for Healthcare investigates how healthcare organizations can leverage this tapestry of big data to discover new business value, use cases, and knowledge as well as how big data can be woven into pre-existing business intelligence and analytics efforts. This book focuses on teaching you how to: Develop skills needed to identify and demolish big-data myths Become an expert in separating hype from reality Understand the V’s that matter in healthcare and why Harmonize the 4 C’s across little and big data Choose data fi delity over data quality Learn how to apply the NRF Framework Master applied machine learning for healthcare Conduct a guided tour of learning algorithms Recognize and be prepared for the future of artificial intelligence in healthcare via best practices, feedback loops, and contextually intelligent agents (CIAs) The variety of data in healthcare spans multiple business workflows, formats (structured, un-, and semi-structured), integration at point of care/need, and integration with existing knowledge. In order to deal with these realities, the authors propose new approaches to creating a knowledge-driven learning organization-based on new and existing strategies, methods and technologies. This book will address the long-standing challenges in healthcare informatics and provide pragmatic recommendations on how to deal with them.

Data Science for Healthcare

Data Science for Healthcare
Author: Sergio Consoli
Publisher: Springer
Total Pages: 367
Release: 2019-02-23
Genre: Computers
ISBN: 3030052494
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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.

Data Analytics in Medicine: Concepts, Methodologies, Tools, and Applications

Data Analytics in Medicine: Concepts, Methodologies, Tools, and Applications
Author: Management Association, Information Resources
Publisher: IGI Global
Total Pages: 2071
Release: 2019-12-06
Genre: Medical
ISBN: 1799812057
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Advancements in data science have created opportunities to sort, manage, and analyze large amounts of data more effectively and efficiently. Applying these new technologies to the healthcare industry, which has vast quantities of patient and medical data and is increasingly becoming more data-reliant, is crucial for refining medical practices and patient care. Data Analytics in Medicine: Concepts, Methodologies, Tools, and Applications is a vital reference source that examines practical applications of healthcare analytics for improved patient care, resource allocation, and medical performance, as well as for diagnosing, predicting, and identifying at-risk populations. Highlighting a range of topics such as data security and privacy, health informatics, and predictive analytics, this multi-volume book is ideally designed for doctors, hospital administrators, nurses, medical professionals, IT specialists, computer engineers, information technologists, biomedical engineers, data-processing specialists, healthcare practitioners, academicians, and researchers interested in current research on the connections between data analytics in the field of medicine.