Machine Learning in Biomedical Informatics: Optimizing Resource Allocation and Energy Efficiency in Public Hospitals
This paper presents a computational framework that combines supervised machine learning and multi-objective optimization to support data-driven decision-making for resource allocation in public healthcare systems. The study uses real-world data from 51 public hospitals across Southern and Northern I...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11121831/ |
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| author | Agostino Marengo Vito Santamato Massimo Iacoviello |
| author_facet | Agostino Marengo Vito Santamato Massimo Iacoviello |
| author_sort | Agostino Marengo |
| collection | DOAJ |
| description | This paper presents a computational framework that combines supervised machine learning and multi-objective optimization to support data-driven decision-making for resource allocation in public healthcare systems. The study uses real-world data from 51 public hospitals across Southern and Northern Italy, ensuring a representative sample of diverse healthcare structures. It analyzes the interplay between hospital admission volumes, adherence to quality standards, operational efficiency, and energy costs. The framework integrates several predictive models—including Random Forest, Support Vector Machines, and Logistic Regression—developed in Python using the scikit-learn library. Model performance was optimized using calibration techniques, while interpretability was enhanced through SHAP (SHapley Additive exPlanations), which identified the most influential clinical areas affecting both efficiency and energy expenditure. Feature importance results were used in a dual-objective linear optimization function to determine the optimal distribution of hospital admissions across nosological areas, balancing scale efficiency and energy cost reduction. Additionally, a multi-objective genetic algorithm (NSGA-II) was applied to optimize quality standard adherence levels, generating trade-off solutions along a Pareto frontier. The proposed approach offers a transparent, explainable, and scalable tool for healthcare administrators, providing actionable insights for strategic resource allocation. By integrating interpretable machine learning with computational optimization, the model contributes to building sustainable and high-performing hospital systems aligned with both operational and environmental objectives. |
| format | Article |
| id | doaj-art-db8fa4befb2b4a75b115929862b8d263 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-db8fa4befb2b4a75b115929862b8d2632025-08-20T03:07:05ZengIEEEIEEE Access2169-35362025-01-011314233114235710.1109/ACCESS.2025.359737311121831Machine Learning in Biomedical Informatics: Optimizing Resource Allocation and Energy Efficiency in Public HospitalsAgostino Marengo0https://orcid.org/0000-0003-2769-5642Vito Santamato1https://orcid.org/0009-0005-9776-0106Massimo Iacoviello2https://orcid.org/0000-0001-9613-5062Department of Agricultural Sciences, Food, Natural Resources, and Engineering, University of Foggia, Foggia, ItalyDepartment of Agricultural Sciences, Food, Natural Resources, and Engineering, University of Foggia, Foggia, ItalyDepartment of Medicine, University of Foggia, Foggia, ItalyThis paper presents a computational framework that combines supervised machine learning and multi-objective optimization to support data-driven decision-making for resource allocation in public healthcare systems. The study uses real-world data from 51 public hospitals across Southern and Northern Italy, ensuring a representative sample of diverse healthcare structures. It analyzes the interplay between hospital admission volumes, adherence to quality standards, operational efficiency, and energy costs. The framework integrates several predictive models—including Random Forest, Support Vector Machines, and Logistic Regression—developed in Python using the scikit-learn library. Model performance was optimized using calibration techniques, while interpretability was enhanced through SHAP (SHapley Additive exPlanations), which identified the most influential clinical areas affecting both efficiency and energy expenditure. Feature importance results were used in a dual-objective linear optimization function to determine the optimal distribution of hospital admissions across nosological areas, balancing scale efficiency and energy cost reduction. Additionally, a multi-objective genetic algorithm (NSGA-II) was applied to optimize quality standard adherence levels, generating trade-off solutions along a Pareto frontier. The proposed approach offers a transparent, explainable, and scalable tool for healthcare administrators, providing actionable insights for strategic resource allocation. By integrating interpretable machine learning with computational optimization, the model contributes to building sustainable and high-performing hospital systems aligned with both operational and environmental objectives.https://ieeexplore.ieee.org/document/11121831/Machine learninghospital optimizationenergy efficiencymultiobjective optimizationSHAP explainabilitypredictive analytics |
| spellingShingle | Agostino Marengo Vito Santamato Massimo Iacoviello Machine Learning in Biomedical Informatics: Optimizing Resource Allocation and Energy Efficiency in Public Hospitals IEEE Access Machine learning hospital optimization energy efficiency multiobjective optimization SHAP explainability predictive analytics |
| title | Machine Learning in Biomedical Informatics: Optimizing Resource Allocation and Energy Efficiency in Public Hospitals |
| title_full | Machine Learning in Biomedical Informatics: Optimizing Resource Allocation and Energy Efficiency in Public Hospitals |
| title_fullStr | Machine Learning in Biomedical Informatics: Optimizing Resource Allocation and Energy Efficiency in Public Hospitals |
| title_full_unstemmed | Machine Learning in Biomedical Informatics: Optimizing Resource Allocation and Energy Efficiency in Public Hospitals |
| title_short | Machine Learning in Biomedical Informatics: Optimizing Resource Allocation and Energy Efficiency in Public Hospitals |
| title_sort | machine learning in biomedical informatics optimizing resource allocation and energy efficiency in public hospitals |
| topic | Machine learning hospital optimization energy efficiency multiobjective optimization SHAP explainability predictive analytics |
| url | https://ieeexplore.ieee.org/document/11121831/ |
| work_keys_str_mv | AT agostinomarengo machinelearninginbiomedicalinformaticsoptimizingresourceallocationandenergyefficiencyinpublichospitals AT vitosantamato machinelearninginbiomedicalinformaticsoptimizingresourceallocationandenergyefficiencyinpublichospitals AT massimoiacoviello machinelearninginbiomedicalinformaticsoptimizingresourceallocationandenergyefficiencyinpublichospitals |