Potential identification of pediatric asthma patients within pediatric research database using low rank matrix decomposition

Journal of Clinical Bioinformatics

Teeradache Viangteeravat¹ 

[1] Biomedical Informatics Core, Children’s Foundation Research Institute, Department of Pediatrics, The University of Tennessee Health Science Center, 50 N. Dunlap, 38013, Memphis, TN, USA

关键词: Classification;    Feature extraction;    Data mining;    Machine learning;    Biomedical informatics;    Medical informatics;    Translational research;    Clinical research;   

Others  :  802796
DOI  :  10.1186/2043-9113-3-16

 received in 2013-07-20, accepted in 2013-08-22,  发布年份 2013

【 摘 要 】

Asthma is a prevalent disease in pediatric patients and most of the cases begin at very early years of life in children. Early identification of patients at high risk of developing the disease can alert us to provide them the best treatment to manage asthma symptoms. Often evaluating patients with high risk of developing asthma from huge data sets (e.g., electronic medical record) is challenging and very time consuming, and lack of complex analysis of data or proper clinical logic determination might produce invalid results and irrelevant treatments. In this article, we used data from the Pediatric Research Database (PRD) to develop an asthma prediction model from past All Patient Refined Diagnosis Related Groupings (APR-DRGs) coding assignments. The knowledge gleamed in this asthma prediction model, from both routinely use by physicians and experimental findings, will become fused into a knowledge-based database for dissemination to those involved with asthma patients. Success with this model may lead to expansion with other diseases.

【 授权许可】

   
2013 Viangteeravat; licensee BioMed Central Ltd.

【 图 表 】

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