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Systems Science and Population Health$
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Abdulrahman M. El-Sayed and Sandro Galea

Print publication date: 2017

Print ISBN-13: 9780190492397

Published to Oxford Scholarship Online: March 2017

DOI: 10.1093/acprof:oso/9780190492397.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (www.oxfordscholarship.com). (c) Copyright Oxford University Press, 2019. All Rights Reserved. Under the terms of the licence agreement, an individual user may print out a PDF of a single chapter of a monograph in OSO for personal use (for details see www.oxfordscholarship.com/page/privacy-policy).date: 16 June 2019

Machine Learning

Machine Learning

Chapter:
(p.129) 11 Machine Learning
Source:
Systems Science and Population Health
Author(s):

James H. Faghmous

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780190492397.003.0011

This chapter introduces non-computational scientists to the general field of machine learning and its methods. The chapter begins by outlining the common structure of machine learning applications, and then it highlights some of the most effective machine learning methods. Then there is a discussion of a case study at the intersection of machine learning and epidemiology: Google Flu Tends. At the end of the chapter there are steps given on how to begin creating practical machine learning algorithms for population health. In so doing this chapter sets the stage for the reader to become familiar with machine modeling as a tool for population health.

Keywords:   influenza, machine learning algorithms, web analytics, epidemiology

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