Assessing the value of data for prediction policies: The case of antibiotic prescribing
研究了不同数据组合对机器学习预测尿路感染及抗生素处方政策效果的影响，发现简单人口统计信息能显著提升预测质量，但丰富健康数据能更大程度减少处方，且数据对预测质量和政策效果的回报递减。
We quantify the value of data for the prediction policy problem of reducing antibiotic prescribing to curb antibiotic resistance. Using varying combinations of administrative data, we evaluate machine learning predictions for diagnosing bacterial urinary tract infections and the outcomes of prescription rules based on these predictions. Simple patient demographics improve prediction quality substantially but larger reductions in prescribing can be achieved by making use of rich health data. Our results suggest decreasing returns to data for prediction quality and increasing returns for policy outcomes. Hence, data needs for prediction policy problems must be assessed based on the policy objective and not only on prediction quality.