C4.5: programs for machine learning
C4.5: programs for machine learning
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Using Model Trees for Classification
Machine Learning
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
A taxonomy of sequential pattern mining algorithms
ACM Computing Surveys (CSUR)
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Computer physician order entry (CPOE) systems play an important role in hospital information systems. However, there are still remaining order corrections and deletions, caused by both of changes of patients' condition and operational problems between a CPOE system and medical doctors. Although medical doctors know a relationship between numbers of order entries and order changes, more concrete descriptions about the order changes are required. In this paper, we present a method for obtaining classification rules of the order changes by using characteristic order entry subsequences that are extracted from daily order entry sequences of patients. By combining patients' basic information, numbers of orders, numbers of order corrections and deletions, and the characteristic order entry subsequences, we obtained classification rules for describing the relationship between the numbers and the order entry changes as a case study. By comparing the contents of the classification rules, we discuss about usefulness of the characteristic order entry sub-sequences for analyzing the order changing factors.