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Evaluation of two different models of semi-automatic knowledge acquisition for the medical consultant system CADIAG-II/RHEUMA.

Abstract：

:As part of a plan to promote semi-automatic knowledge acquisition for the medical consultant system CADIAG-II/RHEUMA, this study sought to explore and cope with the variability of results that may be anticipated when performing knowledge acquisition with patient data from different patient settings. Patient data were drawn both from a published study for the classification of rheumatoid arthritis (RA) and from a large database of rheumatological patient charts developed for the CADIAG-II/RHEUMA system. An analysis of the relationships between RA and selected CADIAG-II/RHEUMA symptoms was done using two models. In one of them, we controlled for the differences in baseline frequencies of symptoms and diseases in the two study populations as an important factor influencing the results of the calculations. Other factors that were identified included inconsistent definitions of symptoms and diseases, and the different composition of study groups in the two study populations. By eliminating differences in baseline frequencies as the most important bias, the results obtained from the two different knowledge sources became more consistent. All remaining inconsistencies and uncertainties about the contribution and relative importance of the factors were formalized using fuzzy intervals.

journal_name

Artif Intell Med

journal_title

Artificial intelligence in medicine

authors

Leitich H,Adlassnig KP,Kolarz G

doi

10.1016/s0933-3657(02)00025-8

subject

Has Abstract

pub_date

2002-07-01 00:00:00

pages

215-25

issue

3

eissn

0933-3657

issn

1873-2860

pii

S0933365702000258

journal_volume

25

pub_type

杂志文章

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