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 language | English |
|---|---|
| Title of host publication | 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 |
| Pages | 95-96 |
| Number of pages | 2 |
| DOIs | |
| Publication status | Published - 2009 Dec 1 |
| Event | 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 Duration: 2009 Nov 2 → 2009 Nov 6 |
Publication series
| Name | International Conference on Information and Knowledge Management, Proceedings |
|---|
Other
| Other | 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 |
|---|---|
| Country | China |
| City | Hong Kong |
| Period | 09/11/2 → 09/11/6 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Decision Sciences(all)
- Business, Management and Accounting(all)
Cite this
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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 (International Conference on Information and Knowledge Management, Proceedings).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - An outcome discovery system to determine mortality factors in primary care facilities
AU - Murillo, Jeremias
AU - Song, Min
PY - 2009/12/1
Y1 - 2009/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=74049101497&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=74049101497&partnerID=8YFLogxK
U2 - 10.1145/1651318.1651341
DO - 10.1145/1651318.1651341
M3 - Conference contribution
AN - SCOPUS:74049101497
SN - 9781605588032
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 95
EP - 96
BT - 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
ER -