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Leveraging Data Science for Global Health
- Publication Year :
- 2020
- Publisher :
- Springer Nature; Springer, 2020.
-
Abstract
- 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.
- Subjects :
- Health Informatics
Health Economics
Open Access
Big Data
Machine Learning
Artificial Intelligence
Digital Disease Surveillance
Health Mapping
Health Records for Non-Communicable Diseases
HealthMap
Tools for Clinical Trials
Medical equipment & techniques
Information technology: general issues
Health & safety aspects of IT
Health economics
bic Book Industry Communication::M Medicine::MB Medicine: general issues::MBG Medical equipment & techniques
bic Book Industry Communication::U Computing & information technology::UB Information technology: general issues::UBH Health & safety aspects of IT
bic Book Industry Communication::K Economics, finance, business & management::KC Economics::KCQ Health economics
Subjects
Details
- Language :
- English
- ISBN :
- 978-3-030-47994-7
3-030-47994-3 - ISBNs :
- 9783030479947 and 3030479943
- Database :
- OAPEN Library
- Notes :
- ONIX_20200813_9783030479947_32, , https://www.springer.com/9783030479947
- Publication Type :
- eBook
- Accession number :
- edsoap.20.500.12657.41290
- Document Type :
- book
- Full Text :
- https://doi.org/10.1007/978-3-030-47994-7