Comparison of Association Rule Mining and Crowdsourcing for Automated Generation of a Problem-Medication Knowledge Base

Abstract

Increased amounts of data contained in electronic health records (EHRs) has led to inefficiencies for clinicians trying to locate relevant patient information. Automated summarization tools that create condition-specific data displays rather than current displays by data type have the potential to greatly improve clinician efficiency. These tools require new kinds of clinical knowledge (e.g., problem-medication relationships) that is difficult to obtain. We compared association rule mining and crowdsourcing as automated methods for generating a knowledge base of problem-medication pairs using a single source of clinical data from a commercially available EHR. The association rule mining and crowdsourcing approaches identified 19,586 and 31,440 pairs respectively. When comparing the top 500 pairs from each approach, only 186 overlapped. Manual inspection of the pairs indicated that crowdsourcing identified mostly common relationships, while association rule mining identified a combination of common and rare relationships. These findings indicate that the approaches are complementary, and further research is necessary to combine the approaches and better evaluate the approaches to generate an all-inclusive, highly accurate problem-medication knowledge base.