Potential identification of pediatric asthma patients within pediatric research database using low rank matrix decompositionReport as inadecuate




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Journal of Clinical Bioinformatics

, 3:16

First Online: 28 September 2013Received: 20 July 2013Accepted: 22 August 2013DOI: 10.1186-2043-9113-3-16

Cite this article as: Viangteeravat, T. J Clin Bioinform 2013 3: 16. doi:10.1186-2043-9113-3-16

Abstract

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.

KeywordsClinical research Translational research Medical informatics Biomedical informatics Machine learning Data mining Feature extraction Classification Electronic supplementary materialThe online version of this article doi:10.1186-2043-9113-3-16 contains supplementary material, which is available to authorized users.

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Author: Teeradache Viangteeravat

Source: https://link.springer.com/



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