Cloud based DevOps Framework for Identifying Risk Factors of Hospital Utilization
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913799683964928 |
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| author | Banerjee, Monojit Hazarika, Akaash Vishal Shah, Mahak |
| author_facet | Banerjee, Monojit Hazarika, Akaash Vishal Shah, Mahak |
| contents | A scalable and reliable system is required to analyze the National Health and Nutrition Examination Survey (NHANES) data efficiently to understand hospital utilization risk factors. This study aims to investigate the integration of continuous integration and deployment (CI/CD) practices in data science workflows, specifically focusing on analyzing NHANES data to identify the prevalence of diabetes, obesity, and cardiovascular diseases. An end-to-end cloud-based DevOps framework is proposed for data analysis which examines risk factors associated with hospital utilization and evaluates key hospital utilization metrics. We have also highlighted the modular structure of the framework that can be generalized for any other domains beyond healthcare. In the framework, an online data update method is provided which can be extended further using both real and synthetic data. As such, the framework can be especially useful for sparse dataset domains such as environmental science, robotics, cybersecurity, and cultural heritage and arts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_14097 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cloud based DevOps Framework for Identifying Risk Factors of Hospital Utilization Banerjee, Monojit Hazarika, Akaash Vishal Shah, Mahak Computers and Society Distributed, Parallel, and Cluster Computing A scalable and reliable system is required to analyze the National Health and Nutrition Examination Survey (NHANES) data efficiently to understand hospital utilization risk factors. This study aims to investigate the integration of continuous integration and deployment (CI/CD) practices in data science workflows, specifically focusing on analyzing NHANES data to identify the prevalence of diabetes, obesity, and cardiovascular diseases. An end-to-end cloud-based DevOps framework is proposed for data analysis which examines risk factors associated with hospital utilization and evaluates key hospital utilization metrics. We have also highlighted the modular structure of the framework that can be generalized for any other domains beyond healthcare. In the framework, an online data update method is provided which can be extended further using both real and synthetic data. As such, the framework can be especially useful for sparse dataset domains such as environmental science, robotics, cybersecurity, and cultural heritage and arts. |
| title | Cloud based DevOps Framework for Identifying Risk Factors of Hospital Utilization |
| topic | Computers and Society Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2504.14097 |