title: Hierarchical classification of large-scale patient records for automatic treatment stratification.
abstract: In this paper, a hierarchical learning algorithm is developed for classifying large-scale patient records, e.g., categorizing large-scale patient records into large numbers of known patient categories (i.e., thousands of known patient categories) for automatic treatment stratification. Our hierarchical learning algorithm can leverage tree structure to train more discriminative max-margin classifiers for high-level nodes and control interlevel error propagation effectively. By ruling out unlikely groups of patient categories (i.e., irrelevant high-level nodes) at an early stage, our hierarchical approach can achieve log-linear computational complexity, which is very attractive for big data applications. Our experiments on one specific medical domain have demonstrated that our hierarchical approach can achieve very competitive results on both classification accuracy and computational efficiency as compared with other state-of-the-art techniques.
authors: Kuizhi
authors: K
authors: Mei
authors: Jinye
authors: J
authors: Peng
authors: Ling
authors: L
authors: Gao
authors: Naiquan Nigel
authors: NN
authors: Zheng
authors: Jianping
authors: J
authors: Fan
mesh_terms: D003663:Decision Trees; D057286:Electronic Health Records / Q000145:classification*; D006801:Humans; D008492:Medical Informatics Computing; D060388:Support Vector Machine