Predicting high health-cost users among people with cardiovascular disease using machine learning and nationwide linked social administrative datasets
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Abstract
Suggested Citation
DOI: 10.1186/s13561-023-00422-1
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References listed on IDEAS
- Andrew H. Briggs, 2022. "Healing the past, reimagining the present, investing in the future: What should be the role of race as a proxy covariate in health economics informed health care policy?," Health Economics, John Wiley & Sons, Ltd., vol. 31(10), pages 2115-2119, October.
- Steve Ryder & Kathleen Fox & Pratik Rane & Nigel Armstrong & Ching-Yun Wei & Sohan Deshpande & Lisa Stirk & Yi Qian & Jos Kleijnen, 2019. "A Systematic Review of Direct Cardiovascular Event Costs: An International Perspective," PharmacoEconomics, Springer, vol. 37(7), pages 895-919, July.
- Thomas G. McGuire & Anna L. Zink & Sherri Rose, 2020. "Simplifying and Improving the Performance of Risk Adjustment Systems," NBER Working Papers 26736, National Bureau of Economic Research, Inc.
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More about this item
Keywords
Machine learning; High-cost users; CVD cost prediction; Health and social administrative data; New Zealand;All these keywords.
JEL classification:
- C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
- I15 - Health, Education, and Welfare - - Health - - - Health and Economic Development
- N37 - Economic History - - Labor and Consumers, Demography, Education, Health, Welfare, Income, Wealth, Religion, and Philanthropy - - - Africa; Oceania
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