Acceptance of rules generated by machine learning among medical experts.
OBJECTIVE
The aim was to evaluate the potential for monotonicity constraints to bias machine learning systems to learn rules that were both accurate and meaningful.


METHODS
Two data sets, taken from problems as diverse as screening for dementia and assessing the risk of mental retardation, were collected and a rule learning system, with and without monotonicity constraints, was run on each. The rules were shown to experts, who were asked how willing they would be to use such rules in practice. The accuracy of the rules was also evaluated.


RESULTS
Rules learned with monotonicity constraints were at least as accurate as rules learned without such constraints. Experts were, on average, more willing to use the rules learned with the monotonicity constraints.


CONCLUSIONS
The analysis of medical databases has the potential of improving patient outcomes and/or lowering the cost of health care delivery. Various techniques, from statistics, pattern recognition, machine learning, and neural networks, have been proposed to "mine" this data by uncovering patterns that may be used to guide decision making. This study suggests cognitive factors make learned models coherent and, therefore, credible to experts. One factor that influences the acceptance of learned models is consistency with existing medical knowledge.
Pazzani
M J
MJ
Department of Information and Computer Science, University of California, Irvine, USA. pazzani@ics.uci.edu
Mani
S
S
Shankle
W R
WR
D000544:Alzheimer Disease / Q000175:diagnosis; D001185:Artificial Intelligence; D003625:Data Collection; D019992:Databases as Topic; D020000:Decision Support Systems, Clinical; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D008607:Intellectual Disability / Q000175:diagnosis; D008609:Mental Status Schedule