Sequential Pattern Mining on Electronic Medical Records for Finding Optimal Clinical Pathways

Abstract: Electronic Medical Records (EMRs) are digital versions of paper charts, used to record the treatment of different patients in hospitals. Clinical pathways are used as guidelines for how to treat different diseases, determined by observing outcomes from previous treatments. Sequential pattern mining is a version of data mining where the data mined is organized in sequences. It is a common research topic in data mining with many new variations on existing algorithms being introduced frequently. In a previous report, the sequential pattern mining algorithm PrefixSpan was used to mine patterns in EMRs to verify or suggest new clinical pathways. It was found to only be able to verify pathways partially. One of the reasons stated for this was that PrefixSpan was too inefficient to be able to mine at a low enough support to consider some items. In this report CSpan is used instead, since it is supposed to outperform PrefixSpan by up to two orders of magnitude, in order to improve runtime and thereby address the problems mentioned in the previous work. The results show that CSpan did indeed improve the runtime and the algorithm was able to mine at a lower minimum support. However, the output was only barely improved.