Medical temporal-knowledge discovery via temporal abstraction

Medical knowledge includes frequently occurring temporal patterns in longitudinal patient records. These patterns are not easily detectable by human clinicians. Current knowledge could be extended by automated temporal data mining. However, multivariate time-oriented data are often present at various levels of abstraction and at multiple temporal granularities, requiring a transformation into a more abstract, yet uniform dimension suitable for mining. Temporal abstraction (of both the time and value dimensions) can transform multiple types of point-based data into a meaningful, time-interval-based data representation, in which significant, interval-based temporal patterns can be discovered. We introduce a modular, fast time-interval mining method, KarmaLego, which exploits the transitivity inherent in temporal relations. We demonstrate the usefulness of KarmaLego in finding meaningful temporal patterns within a set of records of diabetic patients; several patterns seem to have a different frequency depending on gender. We also suggest additional uses of the discovered patterns for temporal clustering of the mined population and for classifying multivariate time series.