Meta-Explainable Machine Learning Model for Public Health Care Resource Management
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| Publicado no: | Journal of Database Management vol. 36, no. 1 (2025), p. 1-29 |
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| Autor principal: | |
| Publicado em: |
IGI Global
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| Assuntos: | |
| Acesso em linha: | Citation/Abstract Full Text - PDF |
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| 003 | UK-CbPIL | ||
| 022 | |a 1063-8016 | ||
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| 022 | |a 1047-9430 | ||
| 024 | 7 | |a 10.4018/JDM.386382 |2 doi | |
| 035 | |a 3236218265 | ||
| 045 | 2 | |b d20250101 |b d20250331 | |
| 084 | |a 11186 |2 nlm | ||
| 100 | 1 | |a Patel, Shivshanker Singh |u Indian Institute of Management, Visakhapatnam, India | |
| 245 | 1 | |a Meta-Explainable Machine Learning Model for Public Health Care Resource Management | |
| 260 | |b IGI Global |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a This study introduces a new meta-explainable machine learning methodology to enhance medical care recommendations and optimize healthcare operations through targeted interventions. It could assist a large, and diverse population facing challenges in resource allocation and operational complexity. The proposed method utilizes a two-stage model. It first employs an Explainable Boosting Machine (EBM) and then provides the output from the initial phase to an unsupervised machine learning framework. It examines diverse aspects to identify the most critical set of features for focused operations and policy recommendations in designated areas. The research is based on data collected from three regions of India about maternal health and maternal mortality. The results highlight the accuracy of healthcare operations, thereby facilitating data-informed decisions. Implementing the method outlined in this paper in any other region across the globe will significantly enhance the design and execution of targeted healthcare initiatives, enhancing public health outcomes and optimizing resource. | |
| 653 | |a Womens health | ||
| 653 | |a Public health | ||
| 653 | |a Health care policy | ||
| 653 | |a Maternal mortality | ||
| 653 | |a Health services | ||
| 653 | |a Machine learning | ||
| 653 | |a Unsupervised learning | ||
| 653 | |a Regions | ||
| 653 | |a Resource allocation | ||
| 653 | |a Health status | ||
| 653 | |a Resource management | ||
| 653 | |a Health initiatives | ||
| 653 | |a Global health | ||
| 653 | |a COVID-19 | ||
| 653 | |a Clinical outcomes | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Maternal & child health | ||
| 653 | |a Health care | ||
| 653 | |a Maternal characteristics | ||
| 653 | |a Decision making | ||
| 653 | |a Optimization | ||
| 653 | |a Mothers | ||
| 773 | 0 | |t Journal of Database Management |g vol. 36, no. 1 (2025), p. 1-29 | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3236218265/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3236218265/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |