X
feed icon rss

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    Online Resource
    Online Resource
    Leveraging Data Science for Global Health (2020)
    Cham : Springer International Publishing | Cham : Springer
    Details
    UID:
    b3kat_BV046835511
    Format: 1 Online-Ressource (XII, 475 Seiten)
    Edition: 1st ed. 2020
    ISBN: 9783030479947
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-47993-0
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-47995-4
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-47996-1
    Language: English
    Subjects: Computer Science
    RVK:
    ST 640
    Keywords: Medizin ; Informatik
    DOI: 10.1007/978-3-030-47994-7
    URL: Volltext  (kostenfrei)
    URL: Volltext  (kostenfrei)
    URL: Buchcover
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    Fulltext
    Fulltext
    EUV Frankfurt
    HTW Berlin
    FH Potsdam
    BTU Cottbus
  • 2
    Online Resource
    Online Resource
    Leveraging Data Science for Global Health (2020)
    [Erscheinungsort nicht ermittelbar] : Springer Nature
    Details
    UID:
    gbv_1778465366
    Format: 1 Online-Ressource (475 p.)
    ISBN: 9783030479947
    Content: 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
    Note: English
    Language: English
    URL: kostenfrei
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    Stabi Berlin
    UB Potsdam
  • 3
    Online Resource
    Online Resource
    Leveraging Data Science for Global Health (2020)
    Cham : Springer International Publishing AG
    Details
    UID:
    gbv_1813332142
    Format: 1 online resource (471 pages)
    ISBN: 9783030479947
    Note: Description based on publisher supplied metadata and other sources
    Additional Edition: ISBN 9783030479930
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9783030479930
    Language: English
    Keywords: Electronic books ; Electronic books
    URL: lizenzpflichtig
    URL: FULL  ((OIS Credentials Required))
    URL: FULL  ((OIS Credentials Required))
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    Berlin International
    UB Potsdam
  • 4
    Online Resource
    Online Resource
    Leveraging Data Science for Global Health / (2020)
    Springer Nature | Cham :Springer International Publishing :
    Details
    UID:
    almafu_9959633368102883
    Format: 1 online resource (XII, 475 p. 196 illus., 175 illus. in color.)
    Edition: 1st ed. 2020.
    ISBN: 3-030-47994-3
    Content: 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.
    Note: Part 1: Big Data and Global Health Landscape -- Chapter 1. Strengths and Weaknesses of Big Data for Global Health Surveillance -- Chapter 2. Opportunities for Health Big Data in Africa -- Chapter 3. HealthMap and Digital Disease Surveillance -- Chapter 4. Mobility Data and Genomics for Disease Surveillance -- Part 2: Case Studies -- Chapter 5. Kumbh Mela Disease Surveillance -- Chapter 6. Using Google Mobility Data for Disaster Monitoring in Puerto Rico -- Chapter 7. StreetRx and the Opioid Epidemic -- Chapter 8. Twitter Data for Zika Virus Surveillance in Venezuela -- Chapter 9. Hepatitis E Outbreak in Namibia and Google Trends -- Chapter 10. Patient-Controlled Health Records for Non-Communicable Diseases in Humanitarian Settings -- Chapter 11. Addressing Sexual and Reproductive Health among Youth Migrants -- Chapter 12. Tanzanian cholera: epidemic or endemic? -- Chapter 13. Google Satellite Images to Predict Yellow Fever Incidence in Brazil -- Chapter 14. Feature Selection and Prediction of Treatment Failure in Tuberculosis -- Chapter 15. Tuberculosis, Refugees, and the Politics of Journalistic Objectivity: A qualitative review using HealthMap data -- Chapter 16. Designing Tools to Support the Cutaneous Leishmaniasis Trial in Colombia. , English
    Additional Edition: ISBN 3-030-47993-5
    Language: English
    DOI: 10.1007/978-3-030-47994-7
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    Open Access Request
    FU Berlin
    Charité
  • 5
    Online Resource
    Online Resource
    Leveraging data science for global health / (2020)
    Cham :Springer,
    Details
    UID:
    kobvindex_HPB1182515218
    Format: 1 online resource
    ISBN: 9783030479947 , 3030479943
    Content: 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.
    Note: Part 1: Big Data and Global Health Landscape -- Chapter 1. Strengths and Weaknesses of Big Data for Global Health Surveillance -- Chapter 2. Opportunities for Health Big Data in Africa -- Chapter 3. HealthMap and Digital Disease Surveillance -- Chapter 4. Mobility Data and Genomics for Disease Surveillance -- Part 2: Case Studies -- Chapter 5. Kumbh Mela Disease Surveillance -- Chapter 6. Using Google Mobility Data for Disaster Monitoring in Puerto Rico -- Chapter 7. StreetRx and the Opioid Epidemic -- Chapter 8. Twitter Data for Zika Virus Surveillance in Venezuela -- Chapter 9. Hepatitis E Outbreak in Namibia and Google Trends -- Chapter 10. Patient-Controlled Health Records for Non-Communicable Diseases in Humanitarian Settings -- Chapter 11. Addressing Sexual and Reproductive Health among Youth Migrants -- Chapter 12. Tanzanian cholera: epidemic or endemic? -- Chapter 13. Google Satellite Images to Predict Yellow Fever Incidence in Brazil -- Chapter 14. Feature Selection and Prediction of Treatment Failure in Tuberculosis -- Chapter 15. Tuberculosis, Refugees, and the Politics of Journalistic Objectivity: A qualitative review using HealthMap data -- Chapter 16. Designing Tools to Support the Cutaneous Leishmaniasis Trial in Colombia.
    Additional Edition: Print version: Leveraging data science for global health. Cham : Springer, 2020 3030479935
    Additional Edition: 9783030479930
    Language: English
    Keywords: Electronic books. ; Electronic books.
    DOI: 10.1007/978-3-030-47994-7
    URL: ProQuest Ebook Central
    URL: SpringerLink
    URL: SpringerLink
    URL: Click here to view book
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    Open Access Request
    HPol Brandenburg
  • 6
    Online Resource
    Online Resource
    Leveraging Data Science for Global Health. (2020)
    Cham :Springer International Publishing AG,
    Details
    UID:
    almahu_9949359267402882
    Format: 1 online resource (471 pages)
    ISBN: 9783030479947
    Additional Edition: Print version: Celi, Leo Anthony Leveraging Data Science for Global Health Cham : Springer International Publishing AG,c2020 ISBN 9783030479930
    Language: English
    Keywords: Electronic books.
    URL: Click to View
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    HU Berlin
  • 7
    Online Resource
    Online Resource
    Leveraging Data Science for Global Health / (2020)
    Springer Nature | Cham :Springer International Publishing :
    Details
    UID:
    almahu_9948574970602882
    Format: 1 online resource (XII, 475 p. 196 illus., 175 illus. in color.)
    Edition: 1st ed. 2020.
    ISBN: 3-030-47994-3
    Content: 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.
    Note: Part 1: Big Data and Global Health Landscape -- Chapter 1. Strengths and Weaknesses of Big Data for Global Health Surveillance -- Chapter 2. Opportunities for Health Big Data in Africa -- Chapter 3. HealthMap and Digital Disease Surveillance -- Chapter 4. Mobility Data and Genomics for Disease Surveillance -- Part 2: Case Studies -- Chapter 5. Kumbh Mela Disease Surveillance -- Chapter 6. Using Google Mobility Data for Disaster Monitoring in Puerto Rico -- Chapter 7. StreetRx and the Opioid Epidemic -- Chapter 8. Twitter Data for Zika Virus Surveillance in Venezuela -- Chapter 9. Hepatitis E Outbreak in Namibia and Google Trends -- Chapter 10. Patient-Controlled Health Records for Non-Communicable Diseases in Humanitarian Settings -- Chapter 11. Addressing Sexual and Reproductive Health among Youth Migrants -- Chapter 12. Tanzanian cholera: epidemic or endemic? -- Chapter 13. Google Satellite Images to Predict Yellow Fever Incidence in Brazil -- Chapter 14. Feature Selection and Prediction of Treatment Failure in Tuberculosis -- Chapter 15. Tuberculosis, Refugees, and the Politics of Journalistic Objectivity: A qualitative review using HealthMap data -- Chapter 16. Designing Tools to Support the Cutaneous Leishmaniasis Trial in Colombia. , English
    Additional Edition: ISBN 3-030-47993-5
    Language: English
    DOI: 10.1007/978-3-030-47994-7
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    Open Access Request
    HU Berlin
  • 8
    Online Resource
    Online Resource
    Leveraging Data Science for Global Health / (2020)
    Springer Nature | Cham :Springer International Publishing :
    Details
    UID:
    edoccha_9959633368102883
    Format: 1 online resource (XII, 475 p. 196 illus., 175 illus. in color.)
    Edition: 1st ed. 2020.
    ISBN: 3-030-47994-3
    Content: 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.
    Note: Part 1: Big Data and Global Health Landscape -- Chapter 1. Strengths and Weaknesses of Big Data for Global Health Surveillance -- Chapter 2. Opportunities for Health Big Data in Africa -- Chapter 3. HealthMap and Digital Disease Surveillance -- Chapter 4. Mobility Data and Genomics for Disease Surveillance -- Part 2: Case Studies -- Chapter 5. Kumbh Mela Disease Surveillance -- Chapter 6. Using Google Mobility Data for Disaster Monitoring in Puerto Rico -- Chapter 7. StreetRx and the Opioid Epidemic -- Chapter 8. Twitter Data for Zika Virus Surveillance in Venezuela -- Chapter 9. Hepatitis E Outbreak in Namibia and Google Trends -- Chapter 10. Patient-Controlled Health Records for Non-Communicable Diseases in Humanitarian Settings -- Chapter 11. Addressing Sexual and Reproductive Health among Youth Migrants -- Chapter 12. Tanzanian cholera: epidemic or endemic? -- Chapter 13. Google Satellite Images to Predict Yellow Fever Incidence in Brazil -- Chapter 14. Feature Selection and Prediction of Treatment Failure in Tuberculosis -- Chapter 15. Tuberculosis, Refugees, and the Politics of Journalistic Objectivity: A qualitative review using HealthMap data -- Chapter 16. Designing Tools to Support the Cutaneous Leishmaniasis Trial in Colombia. , English
    Additional Edition: ISBN 3-030-47993-5
    Language: English
    DOI: 10.1007/978-3-030-47994-7
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    Open Access Request
    Charité
  • 9
    Online Resource
    Online Resource
    Leveraging Data Science for Global Health / (2020)
    Springer Nature | Cham :Springer International Publishing :
    Details
    UID:
    edocfu_9959633368102883
    Format: 1 online resource (XII, 475 p. 196 illus., 175 illus. in color.)
    Edition: 1st ed. 2020.
    ISBN: 3-030-47994-3
    Content: 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.
    Note: Part 1: Big Data and Global Health Landscape -- Chapter 1. Strengths and Weaknesses of Big Data for Global Health Surveillance -- Chapter 2. Opportunities for Health Big Data in Africa -- Chapter 3. HealthMap and Digital Disease Surveillance -- Chapter 4. Mobility Data and Genomics for Disease Surveillance -- Part 2: Case Studies -- Chapter 5. Kumbh Mela Disease Surveillance -- Chapter 6. Using Google Mobility Data for Disaster Monitoring in Puerto Rico -- Chapter 7. StreetRx and the Opioid Epidemic -- Chapter 8. Twitter Data for Zika Virus Surveillance in Venezuela -- Chapter 9. Hepatitis E Outbreak in Namibia and Google Trends -- Chapter 10. Patient-Controlled Health Records for Non-Communicable Diseases in Humanitarian Settings -- Chapter 11. Addressing Sexual and Reproductive Health among Youth Migrants -- Chapter 12. Tanzanian cholera: epidemic or endemic? -- Chapter 13. Google Satellite Images to Predict Yellow Fever Incidence in Brazil -- Chapter 14. Feature Selection and Prediction of Treatment Failure in Tuberculosis -- Chapter 15. Tuberculosis, Refugees, and the Politics of Journalistic Objectivity: A qualitative review using HealthMap data -- Chapter 16. Designing Tools to Support the Cutaneous Leishmaniasis Trial in Colombia. , English
    Additional Edition: ISBN 3-030-47993-5
    Language: English
    DOI: 10.1007/978-3-030-47994-7
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    Open Access Request
    FU Berlin
  • 10
    Online Resource
    Online Resource
    Leveraging Data Science for Global Health (2020)
    Cham :Springer International Publishing :
    Details
    UID:
    almahu_9948573756802882
    Format: XII, 475 p. 196 illus., 175 illus. in color. , online resource.
    Edition: 1st ed. 2020.
    ISBN: 9783030479947
    Content: 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.
    Note: Part 1: Big Data and Global Health Landscape -- Chapter 1. Strengths and Weaknesses of Big Data for Global Health Surveillance -- Chapter 2. Opportunities for Health Big Data in Africa -- Chapter 3. HealthMap and Digital Disease Surveillance -- Chapter 4. Mobility Data and Genomics for Disease Surveillance -- Part 2: Case Studies -- Chapter 5. Kumbh Mela Disease Surveillance -- Chapter 6. Using Google Mobility Data for Disaster Monitoring in Puerto Rico -- Chapter 7. StreetRx and the Opioid Epidemic -- Chapter 8. Twitter Data for Zika Virus Surveillance in Venezuela -- Chapter 9. Hepatitis E Outbreak in Namibia and Google Trends -- Chapter 10. Patient-Controlled Health Records for Non-Communicable Diseases in Humanitarian Settings -- Chapter 11. Addressing Sexual and Reproductive Health among Youth Migrants -- Chapter 12. Tanzanian cholera: epidemic or endemic? -- Chapter 13. Google Satellite Images to Predict Yellow Fever Incidence in Brazil -- Chapter 14. Feature Selection and Prediction of Treatment Failure in Tuberculosis -- Chapter 15. Tuberculosis, Refugees, and the Politics of Journalistic Objectivity: A qualitative review using HealthMap data -- Chapter 16. Designing Tools to Support the Cutaneous Leishmaniasis Trial in Colombia.
    In: Springer Nature eBook
    Additional Edition: Printed edition: ISBN 9783030479930
    Additional Edition: Printed edition: ISBN 9783030479954
    Additional Edition: Printed edition: ISBN 9783030479961
    Language: English
    DOI: 10.1007/978-3-030-47994-7
    URL: https://doi.org/10.1007/978-3-030-47994-7
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    Open Access Request
    HU Berlin
Nothing or not found what you are looking for? Please check your search query or use the Interlibrary Loan Search.
Close ⊗
This website uses cookies and the analysis tool Matomo. Further information can be found on the KOBV privacy pages