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oapen-20.500.12657-41290
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dspace
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oapen-20.500.12657-412902020-08-14T01:17:50Z Leveraging Data Science for Global Health Celi, Leo Anthony Majumder, Maimuna S. Ordóñez, Patricia Osorio, Juan Sebastian Paik, Kenneth E. Somai, Melek 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 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. 2020-08-13T11:53:59Z 2020-08-13T11:53:59Z 2020 book ONIX_20200813_9783030479947_32 https://library.oapen.org/handle/20.500.12657/41290 eng application/pdf n/a 2020_Book_LeveragingDataScienceForGlobal.pdf https://www.springer.com/9783030479947 Springer Nature Springer 10.1007/978-3-030-47994-7 10.1007/978-3-030-47994-7 6c6992af-b843-4f46-859c-f6e9998e40d5 Springer 475 open access
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| institution |
OAPEN
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DSpace
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| language |
English
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| description |
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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| title |
2020_Book_LeveragingDataScienceForGlobal.pdf
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| spellingShingle |
2020_Book_LeveragingDataScienceForGlobal.pdf
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| title_short |
2020_Book_LeveragingDataScienceForGlobal.pdf
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| title_full |
2020_Book_LeveragingDataScienceForGlobal.pdf
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| title_fullStr |
2020_Book_LeveragingDataScienceForGlobal.pdf
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| title_full_unstemmed |
2020_Book_LeveragingDataScienceForGlobal.pdf
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| title_sort |
2020_book_leveragingdatascienceforglobal.pdf
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| publisher |
Springer Nature
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| publishDate |
2020
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| url |
https://www.springer.com/9783030479947
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| _version_ |
1771297474464448512
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