Overview
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.
Full Product Details
Author: Leo Anthony Celi ,
Maimuna S. Majumder ,
Patricia Ordóñez ,
Juan Sebastian Osorio
Publisher: Springer Nature Switzerland AG
Imprint: Springer Nature Switzerland AG
Edition: 1st ed. 2020
Dimensions:
Width: 15.50cm
, Height: 2.50cm
, Length: 23.50cm
Weight: 0.741kg
ISBN: 9783030479961
ISBN 10: 303047996
Pages: 475
Publication Date: 18 September 2020
Audience:
Professional and scholarly
,
Professional & Vocational
Format: Paperback
Publisher's Status: Active
Availability: Manufactured on demand
We will order this item for you from a manufactured on demand supplier.
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)
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)
Author Information
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.
