利用开放医疗数据构建医疗成本预测模型
Building predictive models of healthcare costs with open healthcare data
AI总结：
针对全球医疗成本上升问题，本文利用纽约州SPARCS的230万条去标识化患者数据，基于诊断和人口统计学特征，采用稀疏回归和决策树构建成本预测模型，深度为10的决策树取得0.76的R方值，优于现有文献结果。
摘要：
由于全球医疗成本迅速上升，控制成本引起了广泛关注。一个重要方面是价格透明度，初步努力表明患者会寻找更低成本，从而提高效率。这需要使数据可用，并构建能够预测广泛患者人口统计学特征和病情的医疗成本模型。我们通过使用机器学习技术开发预测模型来解决这一问题。我们分析了来自纽约州SPARCS（全州规划和研究合作系统）的去标识化患者数据，包含2016年的230万条记录。我们构建了基于患者诊断和人口统计学特征预测成本的模型。我们研究了由稀疏回归和决策树组成的两类模型。我们通过使用深度为10的决策树获得了最佳性能。我们获得了0.76的R方值，优于文献中针对类似问题报告的值。
英文摘要：
Due to rapidly rising healthcare costs worldwide, there is significant interest in controlling them. An important aspect concerns price transparency, as preliminary efforts have demonstrated that patients will shop for lower costs, driving efficiency. This requires the data to be made available, and models that can predict healthcare costs for a wide range of patient demographics and conditions. We present an approach to this problem by developing a predictive model using machine-learning techniques. We analyzed de-identified patient data from New York State SPARCS (statewide planning and research cooperative system), consisting of 2.3 million records in 2016. We built models to predict costs from patient diagnoses and demographics. We investigated two model classes consisting of sparse regression and decision trees. We obtained the best performance by using a decision tree with depth 10. We obtained an R-square value of 0.76 which is better than the values reported in the literature for similar problems.
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