Learning multiple diagnosis codes for ICU patients with local disease correlation mining

Wang, Sen, Li, Xue, Chang, Xiaojun, Yao, Lina, Sheng, Quan Z. and Long, Guodong (2017) Learning multiple diagnosis codes for ICU patients with local disease correlation mining. ACM Transactions on Knowledge Discovery from Data, 11 3: . doi:10.1145/3003729


Author Wang, Sen
Li, Xue
Chang, Xiaojun
Yao, Lina
Sheng, Quan Z.
Long, Guodong
Title Learning multiple diagnosis codes for ICU patients with local disease correlation mining
Journal name ACM Transactions on Knowledge Discovery from Data   Check publisher's open access policy
ISSN 1556-472X
1556-4681
Publication date 2017-03-01
Sub-type Article (original research)
DOI 10.1145/3003729
Open Access Status Not yet assessed
Volume 11
Issue 3
Total pages 21
Place of publication New York, NY, United States
Publisher A C M Special Interest Group
Collection year 2018
Language eng
Abstract 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 our algorithm with representative multi-label learning algorithms. Evaluation results have shown that our proposed method has state-of-the-art performance in the annotation of multiple diagnostic codes for ICU patients. This study suggests that problems in the automated diagnosis code annotation can be reliably addressed by using a multi-label learning model that exploits disease correlation. The findings of this study will greatly benefit health care and management in ICU considering that the automated diagnosis code annotation can significantly improve the quality and management of health care for both patients and caregivers.
Keyword Diagnosis code annotation
ICU data mining
Local correlation exploiting
MIMIC II database
Multi-label learning
Pattern discovery
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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