Machine learning and public health policy evaluation: research dynamics and prospects for challenges
BackgroundPublic health policy evaluation is crucial for improving health outcomes, optimizing healthcare resource allocation, and ensuring fairness and transparency in decision-making. With the rise of big data, traditional evaluation methods face new challenges, requiring innovative approaches.Met...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1502599/full |
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| author | Zhengyin Li Hui Zhou Zhen Xu Qingyang Ma |
| author_facet | Zhengyin Li Hui Zhou Zhen Xu Qingyang Ma |
| author_sort | Zhengyin Li |
| collection | DOAJ |
| description | BackgroundPublic health policy evaluation is crucial for improving health outcomes, optimizing healthcare resource allocation, and ensuring fairness and transparency in decision-making. With the rise of big data, traditional evaluation methods face new challenges, requiring innovative approaches.MethodsThis article reviews the principles, scope, and limitations of traditional public health policy evaluation methods and explores the application of machine learning in evaluating public health policies. It analyzes the specific steps for applying machine learning and provides practical examples. The challenges discussed include model interpretability, data bias, the continuation of historical health inequities, and data privacy concerns, while proposing ways to better apply machine learning in the context of big data.ResultsMachine learning techniques hold promise in overcoming some limitations of traditional methods, offering more precise evaluations of public health policies. However, challenges such as lack of model interpretability, the perpetuation of health inequities, data bias, and privacy concerns remain significant.DiscussionTo address these challenges, the article suggests integrating data-driven and theory-driven approaches to improve model interpretability, developing multi-level data strategies to reduce bias and mitigate health inequities, ensuring data privacy through technical safeguards and legal frameworks, and employing validation and benchmarking strategies to enhance model robustness and reproducibility. |
| format | Article |
| id | doaj-art-cb0915665f104d28a0feb46ec2afd877 |
| institution | Kabale University |
| issn | 2296-2565 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj-art-cb0915665f104d28a0feb46ec2afd8772025-01-30T14:46:22ZengFrontiers Media S.A.Frontiers in Public Health2296-25652025-01-011310.3389/fpubh.2025.15025991502599Machine learning and public health policy evaluation: research dynamics and prospects for challengesZhengyin Li0Hui Zhou1Zhen Xu2Qingyang Ma3Institute of Agricultural Economics and Development, Chinese Academy of Agricultural Sciences, Beijing, ChinaInstitute of Agricultural Economics and Development, Chinese Academy of Agricultural Sciences, Beijing, ChinaInstitute of Agricultural Economics and Development, Chinese Academy of Agricultural Sciences, Beijing, ChinaSchool of Law, University of Chinese Academy of Social Sciences, Beijing, ChinaBackgroundPublic health policy evaluation is crucial for improving health outcomes, optimizing healthcare resource allocation, and ensuring fairness and transparency in decision-making. With the rise of big data, traditional evaluation methods face new challenges, requiring innovative approaches.MethodsThis article reviews the principles, scope, and limitations of traditional public health policy evaluation methods and explores the application of machine learning in evaluating public health policies. It analyzes the specific steps for applying machine learning and provides practical examples. The challenges discussed include model interpretability, data bias, the continuation of historical health inequities, and data privacy concerns, while proposing ways to better apply machine learning in the context of big data.ResultsMachine learning techniques hold promise in overcoming some limitations of traditional methods, offering more precise evaluations of public health policies. However, challenges such as lack of model interpretability, the perpetuation of health inequities, data bias, and privacy concerns remain significant.DiscussionTo address these challenges, the article suggests integrating data-driven and theory-driven approaches to improve model interpretability, developing multi-level data strategies to reduce bias and mitigate health inequities, ensuring data privacy through technical safeguards and legal frameworks, and employing validation and benchmarking strategies to enhance model robustness and reproducibility.https://www.frontiersin.org/articles/10.3389/fpubh.2025.1502599/fullpublic health policy evaluationmachine learningbig dataDIDRDDSCM |
| spellingShingle | Zhengyin Li Hui Zhou Zhen Xu Qingyang Ma Machine learning and public health policy evaluation: research dynamics and prospects for challenges Frontiers in Public Health public health policy evaluation machine learning big data DID RDD SCM |
| title | Machine learning and public health policy evaluation: research dynamics and prospects for challenges |
| title_full | Machine learning and public health policy evaluation: research dynamics and prospects for challenges |
| title_fullStr | Machine learning and public health policy evaluation: research dynamics and prospects for challenges |
| title_full_unstemmed | Machine learning and public health policy evaluation: research dynamics and prospects for challenges |
| title_short | Machine learning and public health policy evaluation: research dynamics and prospects for challenges |
| title_sort | machine learning and public health policy evaluation research dynamics and prospects for challenges |
| topic | public health policy evaluation machine learning big data DID RDD SCM |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1502599/full |
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