Diagnosis code assignment using sparsity-based disease correlation embedding

Wang, Sen, Chang, Xiaojun, Li, Xue, Long, Guodong, Yao, Lina and Sheng, Quan Z. (2016) Diagnosis code assignment using sparsity-based disease correlation embedding. IEEE Transactions on Knowledge and Data Engineering, 28 12: 3191-3202. doi:10.1109/TKDE.2016.2605687

Author Wang, Sen
Chang, Xiaojun
Li, Xue
Long, Guodong
Yao, Lina
Sheng, Quan Z.
Title Diagnosis code assignment using sparsity-based disease correlation embedding
Journal name IEEE Transactions on Knowledge and Data Engineering   Check publisher's open access policy
ISSN 1041-4347
Publication date 2016-12-01
Sub-type Article (original research)
DOI 10.1109/TKDE.2016.2605687
Open Access Status Not yet assessed
Volume 28
Issue 12
Start page 3191
End page 3202
Total pages 12
Place of publication Piscataway, NJ, United States
Publisher Institute of Electrical and Electronics Engineers
Language eng
Subject 1710 Information Systems
1706 Computer Science Applications
1703 Computational Theory and Mathematics
Abstract With the latest developments in database technologies, it becomes easier to store the medical records of hospital patients from their first day of admission than was previously possible. In Intensive Care Units (ICU) in the modern medical information system can record patient events in relational databases every second. Knowledge mining from these huge volumes of medical data is beneficial to both caregivers and patients. Given a set of electronic patient records, a system that effectively assigns the disease labels can facilitate medical database management and also benefit other researchers, e.g. pathologists. In this paper, we have proposed a framework to achieve that goal. Medical chart and note data of a patient are used to extract distinctive features. To encode patient features, we apply a Bag-of-Words encoding method for both chart and note data. We also propose a model that takes into account both global information and local correlations between diseases. Correlated diseases are characterized by a graph structure that is embedded in our sparsity-based framework. Our algorithm captures the disease relevance when labeling disease codes rather than making individual decision with respect to a specific disease. At the same time, the global optimal values are guaranteed by our proposed convex objective function. Extensive experiments have been conducted on a real-world large-scale ICU database. The evaluation results demonstrate that our method improves multi-label classification results by successfully incorporating disease correlations.
Keyword Disease correlation embedding
ICD code labeling
Multi-label learning
Sparsity-based regularization
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
School of Information Technology and Electrical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 5 times in Thomson Reuters Web of Science Article | Citations
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