Summary
Background: In a prior study, we developed methods for automatically identifying associations
between medications and problems using association rule mining on a large clinical
data warehouse and validated these methods at a single site which used a self-developed
electronic health record. Objective: To demonstrate the generalizability of these methods by validating them at an external
site.
Methods: We received data on medications and problems for 263,597 patients from the University
of Texas Health Science Center at Houston Faculty Practice, an ambulatory practice
that uses the Allscripts Enterprise commercial electronic health record product. We
then conducted association rule mining to identify associated pairs of medications
and problems and characterized these associations with five measures of interestingness:
support, confidence, chi-square, interest and conviction and compared the top-ranked
pairs to a gold standard.
Results: 25,088 medication-problem pairs were identified that exceeded our confidence and
support thresholds. An analysis of the top 500 pairs according to each measure of
interestingness showed a high degree of accuracy for highly-ranked pairs.
Conclusion: The same technique was successfully employed at the University of Texas and accuracy
was comparable to our previous results. Top associations included many medications
that are highly specific for a particular problem as well as a large number of common,
accurate medication-problem pairs that reflect practice patterns.
Citation: Wright A, McCoy A, Henkin S, Flaherty M, Sittig D. Validation of an association rule
mining-based method to infer associations between medications and problems. ppl Clin
Inf 2013; 4: 100–109
http://dx.doi.org/10.4338/ACI-2012-12-RA-0051
Keywords
Problem list - clinical decision support - data mining - automated inference - methodology
