Predicting baby feeding method from unstructured electronic health record data

A. Rao, K. Maiden, Ben Carterette, Deborah B. Ehrenthal
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引用次数: 7

Abstract

Obesity is one of the most important health concerns in United States and is playing an important role in rising rates of chronic health conditions and health care costs. The percentage of the US population affected with childhood obesity and adult obesity has been on a constant upward linear trend for past few decades. According to Center for Disease control and prevention 35.7% of US adults are obese and 17% of children aged 2-19 years are obese. Researchers and health care providers in the US and the rest of world studying obesity are interested in factors affecting obesity. One such interesting factor potentially related to development of obesity is type of feeding provided to babies. In this work we describe an electronic health record (EHR) data set of babies with feeding method contained in the narrative portion of the record. We compare five supervised machine learning algorithms for predicting feeding method as a discrete value based on text in the field. We also compare these algorithms in terms of the classification error and prediction probability estimates generated by them.
从非结构化电子健康记录数据预测婴儿喂养方法
肥胖是美国最重要的健康问题之一,在慢性健康状况和医疗保健费用上升中起着重要作用。在过去的几十年里,受儿童肥胖和成人肥胖影响的美国人口比例一直呈不断上升的线性趋势。根据美国疾病控制与预防中心的数据,35.7%的美国成年人肥胖,17%的2-19岁儿童肥胖。美国和世界其他地区研究肥胖的研究人员和卫生保健提供者对影响肥胖的因素很感兴趣。一个可能与肥胖发展相关的有趣因素是给婴儿提供的喂养方式。在这项工作中,我们描述了婴儿的电子健康记录(EHR)数据集,记录的叙述部分包含喂养方法。我们比较了五种有监督的机器学习算法,这些算法将馈送方法预测为基于现场文本的离散值。我们还比较了这些算法产生的分类误差和预测概率估计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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