Using the Diagnostic Odds Ratio to Select Patterns to Build an Interpretable Pattern-Based Classifier in a Clinical Domain: Multivariate Sequential Pattern Mining Study

Abstract

BACKGROUND: It is important to exploit all available data on patients in settings such as intensive care burn units (ICBUs), where several variables are recorded over time. It is possible to take advantage of the multivariate patterns that model the evolution of patients to predict their survival. However, pattern discovery algorithms generate a large number of patterns, of which only some are relevant for classification. OBJECTIVE: We propose to use the diagnostic odds ratio (DOR) to select multivariate sequential patterns used in the classification in a clinical domain, rather than employing frequency properties. METHODS: We used data obtained from the ICBU at the University Hospital of Getafe, where 6 temporal variables for 465 patients were registered every day during 5 days, and to model the evolution of these clinical variables, we used multivariate sequential patterns by applying 2 different discretization methods for the continuous attributes. We compared 4 ways in which to employ the DOR for pattern selection: (1) we used it as a threshold to select patterns with a minimum DOR; (2) we selected patterns whose differential DORs are higher than a threshold with regard to their extensions; (3) we selected patterns whose DOR CIs do not overlap; and (4) we proposed the combination of threshold and nonoverlapping CIs to select the most discriminative patterns. As a baseline, we compared our proposals with Jumping Emerging Patterns, one of the most frequently used techniques for pattern selection that utilizes frequency properties. RESULTS: We have compared the number and length of the patterns eventually selected, classification performance, and pattern and model interpretability. We show that discretization has a great impact on the accuracy of the classification model, but that a trade-off must be found between classification accuracy and the physicians' capacity to interpret the patterns obtained. We have also identified that the experiments combining threshold and nonoverlapping CIs (Option 4) obtained the fewest number of patterns but also with the smallest size, thus implying the loss of an acceptable accuracy with regard to clinician interpretation. The best classification model according to the trade-off is a JRIP classifier with only 5 patterns (20 items) that was built using unsupervised correlation preserving discretization and differential DOR in a beam search for the best pattern. It achieves a specificity of 56.32% and an area under the receiver operating characteristic curve of 0.767. CONCLUSIONS: A method for the classification of patients' survival can benefit from the use of sequential patterns, as these patterns consider knowledge about the temporal evolution of the variables in the case of ICBU. We have proved that the DOR can be used in several ways, and that it is a suitable measure to select discriminative and interpretable quality patterns.