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SIGNAL-BASED ANALYSIS OF CLINICAL MICROBIOLOGY

Award Information
Agency: Department of Health and Human Services
Branch: National Institutes of Health
Contract: 2R44AI048332-02
Agency Tracking Number: AI048332
Amount: $0.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: N/A
Solicitation Number: N/A
Timeline
Solicitation Year: N/A
Award Year: 2002
Award Start Date (Proposal Award Date): N/A
Award End Date (Contract End Date): N/A
Small Business Information
VECNA TECHNOLOGIES, INC. 6525 BELCREST ROAD, STE 612
HYATTSVILLE, MD 20782
United States
DUNS: N/A
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 DANIEL THEOBALD
 (301) 864-7253
 THEO@VECNA.COM
Business Contact
 DANIEL THEOBALD
Phone: (301) 864-7253
Email: THEO@VECNA.COM
Research Institution
N/A
Abstract

Hospital infections and antimicrobial resistance are scourges of modern society, affecting several million Americans annually and wasting billions of dollars. Timely, accurate data analysis is critical to preventing the spread of infection and resistance. Preliminary results show that treating clinical microbiology data as signals and applying various analytical techniques to them has tremendous potential to improve the detection of outbreaks and shifts in resistance early on, while the possibility for effective intervention still exists. Building on these results, this research aims to fully characterize at least five years of historical microbiology data from three prominent hospitals. This will be used as training data to optimize early detection techniques through the use of genetic algorithms (GAs) using a Linux supercomputing cluster. Surveys will be conducted of the infection control personnel before and after a one year trial of an early prototype real-time monitoring system to determine the impact such a system has on their ability to prevent the spread of infectious disease. Success will be measured by the survey results as well as by the number of outbreaks detected in a timely manner. This research will extend the scientific understanding of GAs, infectious disease surveillance, and cluster detection.

* Information listed above is at the time of submission. *

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