A comparative study: classification vs. user-based collaborative filtering for clinical prediction.
A comparative study: classification vs. user-based collaborative filtering for clinical prediction.
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DOI：
10.1186/s12874-016-0261-9
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发表时间：
2016-12-08
影响因子：
4
通讯作者：
Blair RH
中科院分区：
文献类型：
--
作者：
Hao F;Blair RH
推荐器系统已经显示出对于在各种设置（例如，市场营销、电子商务等）。基于用户的协同过滤是一种流行的推荐系统，它利用个人对项目的先前满意度，以及“相似”的个人的满意度。最近，已经出现了基于协同过滤的推荐系统用于临床风险预测的应用。在这些应用程序中，个体代表患者，项目代表临床数据，其中包括结果。将推荐系统应用于这种类型的问题需要将监督学习问题重铸为无监督学习问题。基本原理是，具有相似临床特征的患者具有相似的疾病风险。随着“大数据”时代的发展，随着生物医学数据在规模和复杂性方面的持续增长，这种类型的方法很可能会得到实现（例如，电子健康记录）。在本研究中，我们着手了解和评估推荐系统的性能，在一个可控的，但现实的设置。将基于用户的协同过滤推荐系统与逻辑回归和随机森林进行比较，这些系统具有不同类型的插补和不同数量的缺失，基于四个不同的公开可用的医疗数据集：国家健康和营养检查调查（NHANES，2011-2012年肥胖），了解预后偏好结局和治疗风险的研究（SUPPORT），慢性肾脏疾病和皮肤病数据。我们还使用模拟数据检查了响应变量中不同程度的缺失和类别不平衡水平下随机缺失（MAR）或完全随机缺失（MCAR）的观察结果的性能。我们的研究结果表明，基于用户的协同过滤是一贯不如逻辑回归和随机森林与不同的插补真实的和模拟数据。当传统的分类方法可行且实用时，研究结果证明了协同过滤用于临床风险预测的谨慎性。在分类是可接受的替代方案的数据集中，CF可能不可取。我们描述了一些与“大数据”相关的自然应用程序，其中CF将是首选，并得出一些见解，为什么在这种情况下可能需要谨慎。本文的在线版本（doi：10.1186/s12874-016-0261-9）包含补充材料，可供授权用户使用。
Recommender systems have shown tremendous value for the prediction of personalized item recommendations for individuals in a variety of settings (e.g., marketing, e-commerce, etc.). User-based collaborative filtering is a popular recommender system, which leverages an individuals’ prior satisfaction with items, as well as the satisfaction of individuals that are “similar”. Recently, there have been applications of collaborative filtering based recommender systems for clinical risk prediction. In these applications, individuals represent patients, and items represent clinical data, which includes an outcome. Application of recommender systems to a problem of this type requires the recasting a supervised learning problem as unsupervised. The rationale is that patients with similar clinical features carry a similar disease risk. As the “Big Data” era progresses, it is likely that approaches of this type will be reached for as biomedical data continues to grow in both size and complexity (e.g., electronic health records). In the present study, we set out to understand and assess the performance of recommender systems in a controlled yet realistic setting. User-based collaborative filtering recommender systems are compared to logistic regression and random forests with different types of imputation and varying amounts of missingness on four different publicly available medical data sets: National Health and Nutrition Examination Survey (NHANES, 2011-2012 on Obesity), Study to Understand Prognoses Preferences Outcomes and Risks of Treatment (SUPPORT), chronic kidney disease, and dermatology data. We also examined performance using simulated data with observations that are Missing At Random (MAR) or Missing Completely At Random (MCAR) under various degrees of missingness and levels of class imbalance in the response variable. Our results demonstrate that user-based collaborative filtering is consistently inferior to logistic regression and random forests with different imputations on real and simulated data. The results warrant caution for the collaborative filtering for the purpose of clinical risk prediction when traditional classification is feasible and practical. CF may not be desirable in datasets where classification is an acceptable alternative. We describe some natural applications related to “Big Data” where CF would be preferred and conclude with some insights as to why caution may be warranted in this context. The online version of this article (doi:10.1186/s12874-016-0261-9) contains supplementary material, which is available to authorized users.
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影响因子：
1.8
作者：
Heitjan, DF;Basu, S
通讯作者：
Basu, S
DOI：
10.1136/amiajnl-2014-002974
发表时间：
2014-11
期刊：
Journal of the American Medical Informatics Association : JAMIA
影响因子：
--
作者：
Margolis R;Derr L;Dunn M;Huerta M;Larkin J;Sheehan J;Guyer M;Green ED
通讯作者：
Green ED
影响因子：
4.8
作者：
Davis, Darcy A.;Chawla, Nitesh V.;Barabasi, Albert-Laszlo
通讯作者：
Barabasi, Albert-Laszlo
影响因子：
37.8
作者：
Scirica, Benjamin M.;Morrow, David A.;Braunwald, Eugene
通讯作者：
Braunwald, Eugene
DOI：
10.1080/03610928008827941
发表时间：
1980-01-01
期刊：
COMMUNICATIONS IN STATISTICS PART A-THEORY AND METHODS
影响因子：
--
作者：
HOSMER, DW;LEMESHOW, S
通讯作者：
LEMESHOW, S