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

Teeradache Viangteeravat

Research output: Contribution to journalArticle

3 Citations (Scopus)

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.

Original languageEnglish (US)
Article number16
JournalJournal of Clinical Bioinformatics
Volume3
Issue number1
DOIs
StatePublished - Sep 28 2013

Fingerprint

Asthma
Databases
Pediatrics
Research
Electronic Health Records
Physicians
Therapeutics

All Science Journal Classification (ASJC) codes

  • Health Informatics

Cite this

Potential identification of pediatric asthma patients within pediatric research database using low rank matrix decomposition. / Viangteeravat, Teeradache.

In: Journal of Clinical Bioinformatics, Vol. 3, No. 1, 16, 28.09.2013.

Research output: Contribution to journalArticle

@article{8f049b7398b94ff98d2a0b09133ef1b5,
title = "Potential identification of pediatric asthma patients within pediatric research database using low rank matrix decomposition",
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.",
author = "Teeradache Viangteeravat",
year = "2013",
month = "9",
day = "28",
doi = "10.1186/2043-9113-3-16",
language = "English (US)",
volume = "3",
journal = "Journal of Clinical Bioinformatics",
issn = "2043-9113",
publisher = "BioMed Central",
number = "1",

}

TY - JOUR

T1 - Potential identification of pediatric asthma patients within pediatric research database using low rank matrix decomposition

AU - Viangteeravat, Teeradache

PY - 2013/9/28

Y1 - 2013/9/28

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84896381134&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84896381134&partnerID=8YFLogxK

U2 - 10.1186/2043-9113-3-16

DO - 10.1186/2043-9113-3-16

M3 - Article

VL - 3

JO - Journal of Clinical Bioinformatics

JF - Journal of Clinical Bioinformatics

SN - 2043-9113

IS - 1

M1 - 16

ER -