Details:

Alternative Title:
Emerg Infect Dis
Personal Author:
Carter, C. N. ; Ronald, N. C. ; Steele, J. H. ; Young, E. ; Taylor, J. P. ; ... More +
Carter, C. N. ; Ronald, N. C. ; Steele, J. H. ; Young, E. ; Taylor, J. P. ; Russell, L. H. ; Eugster, A. K. ; West, J. E. Less -
Description:
Many infectious and parasitic diseases, especially those newly emerging or reemerging, present a difficult diagnostic challenge because of their obscurity and low incidence. Important clues that could lead to an initial diagnosis are often overlooked, misinterpreted, not linked to a disease, or disregarded. We constructed a computer-based decision support system containing 223 infectious and parasitic diseases and used it to conduct a historical intervention study based on field investigation records of 200 cases of human brucellosis and 96 cases of murine typhus that occurred in Texas from 1980 through 1989. Knowledge-based screening showed that the average number of days from the initial patient visit to the time of correct diagnosis was significantly reduced (brucellosis-from 17.9 to 4.5 days, p = 0.0001, murine typhus-from 11.5 to 8.6 days, p = 0.001). This study demonstrates the potential value of knowledge-based patient screening for rare infectious and parasitic diseases.
Subject:
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Artificial Intelligence
Brucellosis
Decision Support Techniques
Diagnosis, Computer-Assisted
Diagnosis, Differential
Humans
Retrospective Studies
Time Factors
Typhus, Endemic Flea-Borne

Source:
Emerg Infect Dis. 3(1):73-76.
Document Type:
Journal Article
Collection(s):
Emerging Infectious Diseases
Main Document Checksum:
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urn:sha256:215b9c54a5de21ac7d96c9c2dd7cebd8adcc51bf054253c1bb487a9bca831bfb
File Type:

Supporting Files

• 	9126449.nxml	txt
  	license.txt	txt

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