A temporal analysis of QMR.
OBJECTIVE
To understand better the trade-offs of not incorporating explicit time in Quick Medical Reference (QMR), a diagnostic system in the domain of general internal medicine, along the dimensions of expressive power and diagnostic accuracy.


METHODS
The study was conducted in two phases. Phase I was a descriptive analysis of the temporal abstractions incorporated in QMR's terms. Phase II was a pseudo-prospective controlled experiment, measuring the effect of history and physical examination temporal content on the diagnostic accuracy of QMR.


METHODS
For each QMR finding that would fit our operational definition of temporal finding, several parameters describing the temporal nature of the finding were assessed, the most important ones being: temporal primitives, time units, temporal uncertainty, processes, and patterns. The history, physical examination, and initial laboratory results of 105 consecutive patients admitted to the Pittsburgh University Presbyterian Hospital were analyzed for temporal content and factors that could potentially influence diagnostic accuracy (these included: rareness of primary diagnosis, case length, uncertainty, spatial/causal information, and multiple diseases).


RESULTS
776 findings were identified as temporal. The authors developed an ontology describing the terms utilized by QMR developers to express temporal knowledge. The authors classified the temporal abstractions found in QMR in 116 temporal types, 11 temporal templates, and a temporal hierarchy. The odds of QMR's making a correct diagnosis in high temporal complexity cases is 0.7 the odds when the temporal complexity is lower, but this result is not statistically significant (95% confidence interval = 0.27-1.83).


CONCLUSIONS
QMR contains extensive implicit time modeling. These results support the conclusion that the abstracted encoding of time in the medical knowledge of QMR does not induce a diagnostic performance penalty.
Aliferis
C F
CF
Section of Medical Informatics, B50A Lothrop Hall, 190 Lothrop Street, University of Pittsburgh, Pittsburgh, PA, USA. cons@alpha.smi.med.pitt.edu
Cooper
G F
GF
Miller
R A
RA
Buchanan
B G
BG
Bankowitz
R
R
Giuse
N
N
D001185:Artificial Intelligence; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted; D003951:Diagnostic Errors; D006801:Humans; D007388:Internal Medicine; D015999:Multivariate Analysis; D016017:Odds Ratio; D010414:Pennsylvania; D013997:Time Factors