Neubauer Sandra, Schreier Gunter, Redmond Stephen J, Lovell Nigel H
Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:6852-5. doi: 10.1109/EMBC.2015.7319967.
Health insurance claims contain valuable information for predicting the future health of a population. Nowadays, with many mature machine learning algorithms, models can be implemented to predict future medical costs and hospitalizations. However, it is well-known that the way in which the data are represented significantly affects the performance of machine learning algorithms. In health insurance claims, key clinical information mainly comes from the associated clinical codes, such as diagnosis codes and procedure codes, which are hierarchically structured. In this study, it is investigated whether the hierarchies of such clinical codes can be utilized to improve predictive performance in the context of predicting future days in hospital. Empirical investigations were done on data sets of different sizes, considering that the frequency of the appearance of lower-level (more specific) clinical codes could vary significantly in populations of different sizes. The use of bagged trees with feature sets that include only basic demographic features, low-level, medium-level, high-level clinical codes, and a full feature set were compared. The main finding from this study is that different hierarchies of clinical codes do not have a significant impact on the predictive power. Some other findings include: 1) Sample size greatly affects the predictive outcome (more observations result in more stable and more accurate outcomes); 2) Combined use of enriched demographic features and clinical features give better performance as compared to using them separately.
医疗保险理赔包含用于预测人群未来健康状况的宝贵信息。如今，借助许多成熟的机器学习算法，可以实施模型来预测未来的医疗费用和住院情况。然而，众所周知，数据的表示方式会显著影响机器学习算法的性能。在医疗保险理赔中，关键临床信息主要来自相关的临床编码，如诊断编码和程序编码，这些编码具有层次结构。在本研究中，我们探讨了在预测未来住院天数的背景下，此类临床编码的层次结构是否可用于提高预测性能。针对不同规模的数据集进行了实证研究，因为在不同规模的人群中，较低级别（更具体）临床编码的出现频率可能会有显著差异。比较了使用仅包含基本人口统计学特征、低级、中级、高级临床编码的特征集以及完整特征集的袋装树。本研究的主要发现是，临床编码的不同层次结构对预测能力没有显著影响。其他一些发现包括：1）样本大小对预测结果有很大影响（更多观测值会带来更稳定、更准确的结果）；2）与单独使用相比，丰富的人口统计学特征和临床特征结合使用性能更好。
