Abstract

Health care data from patients in the Arizona Health Care Cost Containment System, Arizona's Medicaid program, provides a unique opportunity to exploit state-of-the-art data processing and analysis algorithms to mine the data and provide actionable results that can aid cost containment. This work addresses specific challenges in this real-life health care application to build predictive risk models for forecasting future high-cost users. Such predictive risk modeling has received attention in recent years with statistical techniques being the backbone of proposed methods. We survey the literature and propose a novel data mining approach customized for this potent application. Our empirical study indicates that this approach is useful and can benefit further research on cost containment in the health care industry.

Original languageEnglish (US)
Title of host publicationProceedings - 2007 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2007
Pages202-208
Number of pages7
DOIs
StatePublished - 2007
Event2007 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2007 - Fremont, CA, United States
Duration: Nov 2 2007Nov 4 2007

Other

Other2007 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2007
CountryUnited States
CityFremont, CA
Period11/2/0711/4/07

Fingerprint

Cost Control
Health care
Costs and Cost Analysis
Delivery of Health Care
Costs
Health Care Sector
Data Mining
Medicaid
Health Care Costs
Data mining
Research
Industry

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science(all)
  • Biomedical Engineering

Cite this

Moturu, S. T., Johnson, W., & Liu, H. (2007). Predicting future high-cost patients: A real-world risk modeling application. In Proceedings - 2007 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2007 (pp. 202-208). [4413056] https://doi.org/10.1109/BIBM.2007.54

Predicting future high-cost patients : A real-world risk modeling application. / Moturu, Sai T.; Johnson, William; Liu, Huan.

Proceedings - 2007 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2007. 2007. p. 202-208 4413056.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Moturu, ST, Johnson, W & Liu, H 2007, Predicting future high-cost patients: A real-world risk modeling application. in Proceedings - 2007 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2007., 4413056, pp. 202-208, 2007 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2007, Fremont, CA, United States, 11/2/07. https://doi.org/10.1109/BIBM.2007.54
Moturu ST, Johnson W, Liu H. Predicting future high-cost patients: A real-world risk modeling application. In Proceedings - 2007 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2007. 2007. p. 202-208. 4413056 https://doi.org/10.1109/BIBM.2007.54
Moturu, Sai T. ; Johnson, William ; Liu, Huan. / Predicting future high-cost patients : A real-world risk modeling application. Proceedings - 2007 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2007. 2007. pp. 202-208
@inproceedings{3d33ef881eb445a5b0e2ae9d85c46f0f,
title = "Predicting future high-cost patients: A real-world risk modeling application",
abstract = "Health care data from patients in the Arizona Health Care Cost Containment System, Arizona's Medicaid program, provides a unique opportunity to exploit state-of-the-art data processing and analysis algorithms to mine the data and provide actionable results that can aid cost containment. This work addresses specific challenges in this real-life health care application to build predictive risk models for forecasting future high-cost users. Such predictive risk modeling has received attention in recent years with statistical techniques being the backbone of proposed methods. We survey the literature and propose a novel data mining approach customized for this potent application. Our empirical study indicates that this approach is useful and can benefit further research on cost containment in the health care industry.",
author = "Moturu, {Sai T.} and William Johnson and Huan Liu",
year = "2007",
doi = "10.1109/BIBM.2007.54",
language = "English (US)",
isbn = "0769530311",
pages = "202--208",
booktitle = "Proceedings - 2007 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2007",

}

TY - GEN

T1 - Predicting future high-cost patients

T2 - A real-world risk modeling application

AU - Moturu, Sai T.

AU - Johnson, William

AU - Liu, Huan

PY - 2007

Y1 - 2007

N2 - Health care data from patients in the Arizona Health Care Cost Containment System, Arizona's Medicaid program, provides a unique opportunity to exploit state-of-the-art data processing and analysis algorithms to mine the data and provide actionable results that can aid cost containment. This work addresses specific challenges in this real-life health care application to build predictive risk models for forecasting future high-cost users. Such predictive risk modeling has received attention in recent years with statistical techniques being the backbone of proposed methods. We survey the literature and propose a novel data mining approach customized for this potent application. Our empirical study indicates that this approach is useful and can benefit further research on cost containment in the health care industry.

AB - Health care data from patients in the Arizona Health Care Cost Containment System, Arizona's Medicaid program, provides a unique opportunity to exploit state-of-the-art data processing and analysis algorithms to mine the data and provide actionable results that can aid cost containment. This work addresses specific challenges in this real-life health care application to build predictive risk models for forecasting future high-cost users. Such predictive risk modeling has received attention in recent years with statistical techniques being the backbone of proposed methods. We survey the literature and propose a novel data mining approach customized for this potent application. Our empirical study indicates that this approach is useful and can benefit further research on cost containment in the health care industry.

UR - http://www.scopus.com/inward/record.url?scp=49149095604&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=49149095604&partnerID=8YFLogxK

U2 - 10.1109/BIBM.2007.54

DO - 10.1109/BIBM.2007.54

M3 - Conference contribution

AN - SCOPUS:49149095604

SN - 0769530311

SN - 9780769530314

SP - 202

EP - 208

BT - Proceedings - 2007 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2007

ER -