Battling antibiotic resistance: can machine learning improve prescribing?
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Other versions of this item:
- Michael Allan Ribers & Hannes Ullrich, 2019. "Battling Antibiotic Resistance: Can Machine Learning Improve Prescribing?," Papers 1906.03044, arXiv.org.
- Michael A. Ribers & Hannes Ullrich, 2019. "Battling Antibiotic Resistance: Can Machine Learning Improve Prescribing?," Discussion Papers of DIW Berlin 1803, DIW Berlin, German Institute for Economic Research.
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- Jason Abaluck & Leila Agha & David C. Chan Jr & Daniel Singer & Diana Zhu, 2020. "Mastering the Art of Cookbook Medicine: Machine Learning, Randomized Trials, and Misallocation," NBER Working Papers 27467, National Bureau of Economic Research, Inc.
More about this item
Keywordsantibiotic prescribing; prediction policy; machine learning; expert decision-making;
- C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
- C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
- I11 - Health, Education, and Welfare - - Health - - - Analysis of Health Care Markets
- I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
- L38 - Industrial Organization - - Nonprofit Organizations and Public Enterprise - - - Public Policy
- O38 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Government Policy
- Q28 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Government Policy
NEP fieldsThis paper has been announced in the following NEP Reports:
- NEP-BIG-2019-06-24 (Big Data)
- NEP-HEA-2019-06-24 (Health Economics)
- NEP-PAY-2019-06-24 (Payment Systems & Financial Technology)
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