Inductive learning approach to intelligent patient image pre-fetching: extension and evaluation of CN2 algorithm

When performing primary reading on a newly taken radiographic examination, a radiologist often needs to reference relevant prior images of the same patient for confirmation of comparison purposes. To effectively support such prior image references, we proposed an intelligent patient image retrieval system, which includes a learning subsystem for including patient image extended knowledge, based on radiologists image reference behaviors. In this study, we extended CN2, a decision rule-based induction technique, to address challenging characteristics unique to our application, including missing/noisy data and multiple decision outcomes. Our experimental results showed that the overall learning performance of the proposed CN2-MD algorithm was satisfactory, exhibiting desirable noise tolerance, immunity to missing data, and robustness with limited training data.