The use of physician domain knowledge to improve the learning of rule-based models for decision-support.
This paper describes a study testing the hypothesis that the learning of a decision-support model by a computer learning algorithm from clinical data can be improved by the addition of domain knowledge from practicing physicians. The domain of the experiment is community-acquired pneumonia. The overall design of the study compares a computer learning algorithm given clinical data to one given clinical data plus domain knowledge added by physician subjects. This study showed that the performance of the computer-generated models augmented with knowledge added by physician subjects were significantly better than the computer-generated models generated without added knowledge using a two-stage rule induction algorithm in the domain of community-acquired pneumonia. This result was highly significant and shows that the addition of domain knowledge may be beneficial to the learning of clinical decision-support models, especially in domains where data is limited.
Ambrosino
R
R
Center for Biomedical Informatics, University of Pittsburgh, USA.
Buchanan
B G
BG
D000465:Algorithms; D001185:Artificial Intelligence; D017714:Community-Acquired Infections / Q000401:mortality*; D003661:Decision Support Techniques; D005069:Evaluation Studies as Topic; D007722:Health Knowledge, Attitudes, Practice; D017052:Hospital Mortality; D006801:Humans; D010820:Physicians; D010865:Pilot Projects; D011014:Pneumonia / Q000401:mortality*; D012307:Risk Factors