An evaluation of computer assisted clinical classification algorithms.
The Mayo Clinic has a long tradition of indexing patient records in high resolution and volume. Several algorithms have been developed which promise to help human coders in the classification process. We evaluate variations on code browsers and free text indexing systems with respect to their speed and error rates in our production environment. The more sophisticated indexing systems save measurable time in the coding process, but suffer from incompleteness which requires a back-up system or human verification. Expert Network does the best job of rank ordering clinical text, potentially enabling the creation of thresholds for the pass through of computer coded data without human review.
Chute
C G
CG
Section of Medical Information Resources, Mayo Clinic/Foundation, Rochester, MN.
Yang
Y
Y
Buntrock
J
J
D000043:Abstracting and Indexing / Q000379:methods*; D000465:Algorithms; D001185:Artificial Intelligence; D005069:Evaluation Studies as Topic; D005103:Expert Systems; D017143:Fuzzy Logic; D006801:Humans; D016018:Least-Squares Analysis; D008499:Medical Records / Q000145:classification*; D016571:Neural Networks, Computer