Overview
- Is the first and currently the only book on digital disease surveillance through the application of machine learning to non-traditional data sources
- Focuses on combating disease and promoting health, especially in resource-constrained settings
- Includes and expands on the latest non-traditional data sources such as Google Trends, Google Street View, the news media, and social media
- Is an open access book
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About this book
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.
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Keywords
Table of contents (29 chapters)
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Front Matter
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Building a Data Science Ecosystem for Healthcare
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Front Matter
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Health Data Science Workshops
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Front Matter
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Reviews
“This book seems to empower the reader to gradually embark on the development of medical applications incorporating data science. … This book is well structured, written with a good level of linguistic guts, and could be recommended to data science students rather than researchers or health professionals.” (Thierry Edoh, Computing Reviews, March 24, 2022)
Editors and Affiliations
About the editors
Leo Anthony Celi, M.D., M.S., M.P.H., has practiced medicine in three continents, giving him broad perspectives in healthcare delivery. As clinical research director and principal research scientist at the MIT Laboratory for Computational Physiology (LCP) and as an attending physician at the Beth Israel Deaconess Medical Center (BIDMC), he brings together clinicians and data scientists to support research using data routinely collected in the process of care. Leo also founded and co-directs Sana, a cross-disciplinary organization based at the Institute for Medical Engineering and Science at MIT, whose objective is to leverage information technology to improve health outcomes in low- and middle-income countries. He is one of the course directors for global health informatics to improve quality of care, and collaborative data science in medicine, both at MIT. He is an editor of the textbook for each course, both released under an open access license. Leo has spoken in 25 countries about the value of data in improving health outcomes.
Bibliographic Information
Book Title: Leveraging Data Science for Global Health
Editors: Leo Anthony Celi, Maimuna S. Majumder, Patricia Ordóñez, Juan Sebastian Osorio, Kenneth E. Paik, Melek Somai
DOI: https://doi.org/10.1007/978-3-030-47994-7
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s) 2020
Hardcover ISBN: 978-3-030-47993-0Published: 01 August 2020
Softcover ISBN: 978-3-030-47996-1Published: 18 September 2020
eBook ISBN: 978-3-030-47994-7Published: 31 July 2020
Edition Number: 1
Number of Pages: XII, 475
Number of Illustrations: 21 b/w illustrations, 175 illustrations in colour
Topics: Health Informatics, Health Informatics, Health Economics