An outcome discovery system to determine mortality factors in primary care facilities

Jeremias Murillo, Min Song

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

1 Citation (Scopus)

Abstract

This project assembles a virtual team consisting of personnel from the New Jersey Institute of Technology with expertise in the data mining domain and the Saint Barnabas Health Care System with expertise in the medical domain. We apply proven techniques in data and text mining to the problem of hospital mortality. Methodology in outcomes research using data/text mining has typically included Bayesian Networks to include decision trees and rules, regression analysis or Neural Networks/Support Vector Machines to analyze a single disease or condition. We propose to instead analyze the entire spectrum of reasons patients are admitted to a hospital in an effort to discern what chronologies result in good outcomes and which in the worst outcome so as to identify the characteristics to be avoided throughout the spectrum of reasons for admission.

Original languageEnglish
Title of host publication3rd ACM International Workshop on Data and Text Mining in Bioinformatics, DTMBIO'09, Co-located with the 18th ACM International Conference on Information and Knowledge Management, CIKM 2009
Pages95-96
Number of pages2
DOIs
Publication statusPublished - 2009 Dec 1
Event3rd ACM International Workshop on Data and Text Mining in Bioinformatics, DTMBIO'09, Co-located with the 18th ACM International Conference on Information and Knowledge Management, CIKM 2009 - Hong Kong, China
Duration: 2009 Nov 22009 Nov 6

Other

Other3rd ACM International Workshop on Data and Text Mining in Bioinformatics, DTMBIO'09, Co-located with the 18th ACM International Conference on Information and Knowledge Management, CIKM 2009
CountryChina
CityHong Kong
Period09/11/209/11/6

Fingerprint

Mortality
Data mining
Expertise
Text mining
Primary care
Factors
Decision rules
Personnel
Support vector machine
Bayesian networks
Admission
Health care system
Chronology
Decision tree
Regression analysis
Neural networks
Virtual teams
Outcomes research
Methodology

All Science Journal Classification (ASJC) codes

  • Business, Management and Accounting(all)
  • Decision Sciences(all)

Cite this

Murillo, J., & Song, M. (2009). An outcome discovery system to determine mortality factors in primary care facilities. In 3rd ACM International Workshop on Data and Text Mining in Bioinformatics, DTMBIO'09, Co-located with the 18th ACM International Conference on Information and Knowledge Management, CIKM 2009 (pp. 95-96) https://doi.org/10.1145/1651318.1651341
Murillo, Jeremias ; Song, Min. / An outcome discovery system to determine mortality factors in primary care facilities. 3rd ACM International Workshop on Data and Text Mining in Bioinformatics, DTMBIO'09, Co-located with the 18th ACM International Conference on Information and Knowledge Management, CIKM 2009. 2009. pp. 95-96
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Murillo, J & Song, M 2009, An outcome discovery system to determine mortality factors in primary care facilities. in 3rd ACM International Workshop on Data and Text Mining in Bioinformatics, DTMBIO'09, Co-located with the 18th ACM International Conference on Information and Knowledge Management, CIKM 2009. pp. 95-96, 3rd ACM International Workshop on Data and Text Mining in Bioinformatics, DTMBIO'09, Co-located with the 18th ACM International Conference on Information and Knowledge Management, CIKM 2009, Hong Kong, China, 09/11/2. https://doi.org/10.1145/1651318.1651341

An outcome discovery system to determine mortality factors in primary care facilities. / Murillo, Jeremias; Song, Min.

3rd ACM International Workshop on Data and Text Mining in Bioinformatics, DTMBIO'09, Co-located with the 18th ACM International Conference on Information and Knowledge Management, CIKM 2009. 2009. p. 95-96.

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

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Murillo J, Song M. An outcome discovery system to determine mortality factors in primary care facilities. In 3rd ACM International Workshop on Data and Text Mining in Bioinformatics, DTMBIO'09, Co-located with the 18th ACM International Conference on Information and Knowledge Management, CIKM 2009. 2009. p. 95-96 https://doi.org/10.1145/1651318.1651341