Data Mining for Hospital Infection Control
Author Information
Author(s): Stephen A. Moser, Warren T. Jones, Stephen E. Brossette
Primary Institution: University of Alabama at Birmingham
Hypothesis
Can a new data mining process identify unexpected patterns in hospital infection control data?
Conclusion
The Data Mining Surveillance System (DMSS) can identify previously unknown patterns in infection control data.
Supporting Evidence
- DMSS can identify potentially interesting and previously unknown patterns in infection control data.
- Statistical tests were used to compare incidence proportions over time.
- Templates were used to filter out uninteresting association rules.
Takeaway
Researchers created a system that helps hospitals find hidden patterns in infection data, which can help them control infections better.
Methodology
The study used a data mining process to analyze infection control data from the University of Alabama at Birmingham Hospital.
Limitations
Future work is needed to determine the usefulness of DMSS in clinical studies and to improve user interface and data handling.
Participant Demographics
Data was extracted from patients in intensive care units at the University of Alabama at Birmingham Hospital.
Statistical Information
P-Value
0.01
Statistical Significance
p<0.01
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