Machine Learning for Precision Health Economics and Outcomes Research (P-HEOR): Conceptual Review of Applications and Next Steps
Precision health economics and outcomes research (P-HEOR) integrates economic and clinical value assessment by explicitly discovering distinct clinical and health care utilization phenotypes among patients. Through a conceptualized example, the objective of this review is to highlight the capabiliti...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Columbia Data Analytics, LLC
2020-05-01
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| Series: | Journal of Health Economics and Outcomes Research |
| Online Access: | https://doi.org/10.36469/jheor.2020.12698 |
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| _version_ | 1823860339962281984 |
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| author | Yixi Chen Viktor V Chirikov Xiaocong L Marston Jingang Yang Haibo Qiu Jianfeng Xie Ning Sun Chengming Gu Peng Dong Xin Gao |
| author_facet | Yixi Chen Viktor V Chirikov Xiaocong L Marston Jingang Yang Haibo Qiu Jianfeng Xie Ning Sun Chengming Gu Peng Dong Xin Gao |
| author_sort | Yixi Chen |
| collection | DOAJ |
| description | Precision health economics and outcomes research (P-HEOR) integrates economic and clinical value assessment by explicitly discovering distinct clinical and health care utilization phenotypes among patients. Through a conceptualized example, the objective of this review is to highlight the capabilities and limitations of machine learning (ML) applications to P-HEOR and to contextualize the potential opportunities and challenges for the wide adoption of ML for health economics. We outline a P-HEOR conceptual framework extending the ML methodology to comparatively assess the economic value of treatment regimens. Latest methodology developments on bias and confounding control in ML applications to precision medicine are also summarized. |
| format | Article |
| id | doaj-art-4ea1832a4be94f1b90ab371aae3ebcf2 |
| institution | Kabale University |
| issn | 2327-2236 |
| language | English |
| publishDate | 2020-05-01 |
| publisher | Columbia Data Analytics, LLC |
| record_format | Article |
| series | Journal of Health Economics and Outcomes Research |
| spelling | doaj-art-4ea1832a4be94f1b90ab371aae3ebcf22025-02-10T16:13:02ZengColumbia Data Analytics, LLCJournal of Health Economics and Outcomes Research2327-22362020-05-0171Machine Learning for Precision Health Economics and Outcomes Research (P-HEOR): Conceptual Review of Applications and Next StepsYixi ChenViktor V ChirikovXiaocong L MarstonJingang YangHaibo QiuJianfeng XieNing SunChengming GuPeng DongXin GaoPrecision health economics and outcomes research (P-HEOR) integrates economic and clinical value assessment by explicitly discovering distinct clinical and health care utilization phenotypes among patients. Through a conceptualized example, the objective of this review is to highlight the capabilities and limitations of machine learning (ML) applications to P-HEOR and to contextualize the potential opportunities and challenges for the wide adoption of ML for health economics. We outline a P-HEOR conceptual framework extending the ML methodology to comparatively assess the economic value of treatment regimens. Latest methodology developments on bias and confounding control in ML applications to precision medicine are also summarized.https://doi.org/10.36469/jheor.2020.12698 |
| spellingShingle | Yixi Chen Viktor V Chirikov Xiaocong L Marston Jingang Yang Haibo Qiu Jianfeng Xie Ning Sun Chengming Gu Peng Dong Xin Gao Machine Learning for Precision Health Economics and Outcomes Research (P-HEOR): Conceptual Review of Applications and Next Steps Journal of Health Economics and Outcomes Research |
| title | Machine Learning for Precision Health Economics and Outcomes Research (P-HEOR): Conceptual Review of Applications and Next Steps |
| title_full | Machine Learning for Precision Health Economics and Outcomes Research (P-HEOR): Conceptual Review of Applications and Next Steps |
| title_fullStr | Machine Learning for Precision Health Economics and Outcomes Research (P-HEOR): Conceptual Review of Applications and Next Steps |
| title_full_unstemmed | Machine Learning for Precision Health Economics and Outcomes Research (P-HEOR): Conceptual Review of Applications and Next Steps |
| title_short | Machine Learning for Precision Health Economics and Outcomes Research (P-HEOR): Conceptual Review of Applications and Next Steps |
| title_sort | machine learning for precision health economics and outcomes research p heor conceptual review of applications and next steps |
| url | https://doi.org/10.36469/jheor.2020.12698 |
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