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Learning Multiple Diagnosis Codes for ICU Patients with Local Disease Correlation Mining SEN WANG, Griffith University, Australia XUE LI, The University of Queensland, Australia XIAOJUN CHANG*, Carnegie Mellon University, USA LINA YAO, The University of New South Wales QUAN Z. SHENG, Macquarie University, Australia GUODONG LONG, University of Technology Sydney, Australia In the era of big data, a mechanism that can automatically annotate disease codes to patients' records in the medical information system is in demand. The purpose of this work is to propose a framework that automatically annotates the disease labels of multi-source patient data in Intensive Care Units (ICUs). We extract features from two main sources, medical charts and notes. The Bag-of-Words model is used to encode the features. Unlike most of the existing multi-label learning algorithms that globally consider correlations between diseases, our model learns disease correlation locally in the patient data. To achieve this, we derive a local disease correlation representation to enrich the discriminant power of each patient data. This representation is embedded into a unified multi-label learning framework. We develop an alternating algorithm to iteratively optimize the objective function. Extensive experiments have been conducted on a real-world ICU database. We have compared
ACM Transactions on Knowledge Discovery from Data (TKDD) – Association for Computing Machinery
Published: Mar 10, 2017
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