A systematic approach to identify health system resilience indicators using artificial neural network algorithm

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dc.contributor.authorÇakır, Kübra
dc.contributor.authorErol, Özgür
dc.contributor.authorArslan Öztürk, Hatice
dc.date.accessioned2025-12-10T06:33:33Z
dc.date.available2025-12-10T06:33:33Z
dc.date.issued2024
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Endüstri Mühendisliği Bölümü
dc.description.abstractAccording to the World Health Organization (WHO), a health system comprises all organizations, people, and actions that aim to promote, restore, or maintain health [1]. Health systems perform multiple functions in society not only delivering healthcare services and other interventions aimed at maintaining or improving health. They serve multiple societal functions beyond merely delivering healthcare services, but also adapting to risks, disasters, and unpredictable events to maintain functionality. Health system resilience, therefore, refers to the system's capacity to adapt and return to previous performance levels as quickly as possible after such disruptions. The WHO has created a model called Six Building Blocks to measure the health system's resilience. This study aims to assign indicators obtained from a comprehensive literature review to the blocks established by the World Health. The study uses an Artificial Neural Network (ANN) algorithm to analyze data from 35 Organisation for Economic Co-Operation and Development (OECD) countries from 2020 to 2022. ANN is used to assess the varied relevance or weights of each building block's contribution to health system resilience, providing detailed knowledge of how different components impact overall health system performance during crises. As a consequence of this study, policymakers may identify which healthcare building block and accompanying indicator should be prioritized for investment in preparation for future pandemics. The findings suggest which characteristics are most important for increasing health system resilience during public health emergencies.
dc.identifier.citationÇakır, K., Erol, Ö. ve Arslan Öztürk, H. (2024). A systematic approach to identify health system resilience indicators using artificial neural network algorithm. ISSE 2024 - 10th IEEE International Symposium on Systems Engineering, Proceedings, Perugia, 16-18 October 2024. http://dx.doi.org/10.1109/ISSE63315.2024.10741156
dc.identifier.doi10.1109/ISSE63315.2024.10741156
dc.identifier.isbn9798350353723
dc.identifier.scopus2-s2.0-85211920469
dc.identifier.urihttp://dx.doi.org/10.1109/ISSE63315.2024.10741156
dc.identifier.urihttps://hdl.handle.net/20.500.12511/13315
dc.indekslendigikaynakScopus
dc.institutionauthorÇakır, Kübra
dc.institutionauthorErol, Özgür
dc.institutionauthorid0009-0007-4760-7554
dc.institutionauthorid0000-0002-4893-0536
dc.language.isoen
dc.relation.ispartofISSE 2024 - 10th IEEE International Symposium on Systems Engineering, Proceedings
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectArtificial Neural Network
dc.subjectHealth System Resilience
dc.subjectResilience Engineering
dc.subjectSystem Engineering
dc.titleA systematic approach to identify health system resilience indicators using artificial neural network algorithm
dc.typeConference Object

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