Automated reasoning system in histopathologic diagnosis and prognosis of prostate cancer and its precursors.
OBJECTIVE
This article presents the rationale and options offered to diagnostic and prognostic decision support systems for prostate pathology by automated reasoning capabilities.


METHODS
The symbolic information used in diagnostic decision-making is systematically ordered, compared, numerically assessed in its probability, and combined such that a conclusion can be drawn. The framework for the processing of such symbolic information may be an expert system, an inference network or a case-based reasoning system. Automated reasoning is implemented by the use of a rule base and information flow control modules.


RESULTS
Automated reasoning allows decision support systems to follow highly adaptive decision sequences, capable of handling contradictory evidence, exceptions in diagnostic clue expression, and nonmonotonic decision-making.


CONCLUSIONS
Automated reasoning capability in diagnostic and prognostic decision support systems allows highly flexible decision development, very close to human decision procedures.
Bartels
P H
PH
Optical Sciences Center, University of Arizona, Tucson 85721, USA.
Thompson
D
D
Montironi
R
R
Mariuzzi
G
G
Hamilton
P W
PW
D001185:Artificial Intelligence; D016022:Case-Control Studies; D002454:Cell Differentiation / Q000502:physiology; D006801:Humans; D016015:Logistic Models; D008297:Male; D011379:Prognosis; D011467:Prostate / Q000166:cytology / Q000473:pathology*; D011470:Prostatic Hyperplasia / Q000473:pathology; D019048:Prostatic Intraepithelial Neoplasia / Q000473:pathology; D011471:Prostatic Neoplasms / Q000175:diagnosis* / Q000473:pathology