Abstract
Data science promises to revolutionize healthcare, providing insight into disease mechanisms, enabling a more personalized approach to care, improving public health surveillance and the ability to predict trends. However, there are challenges and barriers to its application and implementation, requiring careful attention to data governance and ethics. In this chapter, we present examples of the application of data science in global health and briefly discuss the challenges that should be addressed for its equitable implementation.
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Notes
- 1.
Big data refers to a large volume or quantity of information. In addition to its size, big data is also characterized by its diversity (various formats and types, structured and unstructured) as well as the speed with which it accumulates.
References
Wyber R, Vaillancourt S, Perry W, Mannava P, Folaranmi T, Celi LA. Big data in global health: improving health in low- and middle-income countries. Bull World Health Organ. 2015;93(3):203–8. https://doi.org/10.2471/BLT.14.139022.
Qingpeng Z, Gao J, Wu JT, Cao Z, Zeng DD. Data science approaches to confronting the COVID-19 pandemic: a narrative review. Philos Trans A Math Phys Eng Sci. 2022;380:20210127. https://doi.org/10.1098/rsta.2021.0127.
Hao X, Cheng S, Wu D, Wu T, Lin X, Wang C. Reconstruction of the full transmission dynamics of COVID-19 in Wuhan. Nature. 2020;584(7821):420–4. https://doi.org/10.1038/s41586-020-2554-8.
Kleinman RA, Merkel C. Digital contact tracing for COVID-19. CMAJ. 2020;192(24):E653–6. https://doi.org/10.1503/cmaj.200922.
Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O, Rajendra AU. Automated detection of COVID-19 cases using deep neural networks with x-ray images. Comput Biol Med. 2020;121:103792. https://doi.org/10.1016/j.compbiomed.2020.103792.
Ong E, Wong MU, Huffman A, He Y. COVID-19 coronavirus vaccine design using reverse vaccinology and machine learning. Front Immunol. 2020;11:1581Published 2020 Jul 3. https://doi.org/10.3389/fimmu.2020.01581.
Alrajhi AA, Alswailem OA, Wali G, et al. Data-driven prediction for COVID-19 severity in hospitalized patients. Int J Environ Res Public Health. 2022;19(5):2958. https://doi.org/10.3390/ijerph19052958.
Khan MT, Kaushik AC, Ji L, Malik SI, Ali S, Wei DQ. Artificial neural networks for prediction of tuberculosis disease. Front Microbiol. 2019;10:395. Published 2019 Mar 4. https://doi.org/10.3389/fmicb.2019.00395.
Nagpal MS, Barbaric A, Sherifali D, Morita PP, Cafazzo JA. Patient-generated data analytics of health behaviors of people living with type 2 diabetes: scoping review. JMIR Diabetes. 2021;6(4):e29027. Published 2021 Dec 20. https://doi.org/10.2196/29027.
Achilonu OJ, Fabian J, Bebington B, Singh E, Eijkemans MJC, Musenge E. Predicting colorectal cancer recurrence and patient survival using supervised machine learning approach: a south african population-based study [Published correction appears in front public health]. Front Public Health. 2021;9:694306. https://doi.org/10.3389/fpubh.2021.694306.
Macaulay BO, Aribisala BS, Akande SA, Akinnuwesi BA, Olabanjo OA. Breast cancer risk prediction in African women using random Forest classifier. Cancer Treat Res Commun. 2021;28:100396. https://doi.org/10.1016/j.ctarc.2021.100396.
Li Y, Luo YH, Wampfler JA, et al. Efficient and accurate extracting of unstructured EHRs on cancer therapy responses for the development of RECIST natural language processing tools: part I, the corpus. JCO Clin Cancer Inform. 2020;4:383–91. https://doi.org/10.1200/CCI.19.00147.
Jamthikar AD, et al. Artificial intelligence framework for predictive cardiovascular and stroke risk assessment models: a narrative review of integrated approaches using carotid ultrasound. Comput Biol Med. 2020;126:104043.
Chowdhury AS, Lofgren ET, Moehring RW, Broschat SL. Identifying predictors of antimicrobial exposure in hospitalized patients using a machine learning approach. J Appl Microbiol. 2019;128:688–96. https://doi.org/10.1111/jam.14499.
Lara RAN, Aguilera-Mendoza L, Brizuela CA, Pena A, Rio G. Heterologous machine learning for the identification of antimicrobial activity in human-targeted drugs. Molecules. 2019;24:13. https://doi.org/10.3390/molecules24071258.
Valles S. A brief history of the social concept of health and its role in population health science. In: Philosophy of population health science: philosophy for a new public health era. London: Routledge; 2018. p. 31–56.
Yearby R. Structural racism and health disparities: reconfiguring the social determinants of health framework to include the root cause. J Law Med Ethics. 2020;48(3):518–26. https://doi.org/10.1177/1073110520958876.
COHRED: Research fairness initiative. 2018. https://rfi.cohred.org/. Accessed 22 Jun 2022.
Molldrem S, Smith AK. Reassessing the ethics of molecular HIV surveillance in the era of cluster detection and response: toward HIV data justice. Am J Bioeth. 2020;20(10):10–23.
Martin KE. Ethical issues in the big data industry. MIS Q Exec. 2015;14(2):67–85.
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Meagher, K., Falzon, D., Temesgen, Z. (2023). Data Science for Global Health. In: Raviglione, M.C.B., Tediosi, F., Villa, S., Casamitjana, N., Plasència, A. (eds) Global Health Essentials. Sustainable Development Goals Series. Springer, Cham. https://doi.org/10.1007/978-3-031-33851-9_59
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