Extending a time series retrieval tool to deal with sub-series matching: an application to the hemodialysis domain

The problem of retrieving time series similar to a specified query pattern has been recently addressed within the Case Based Reasoning (CBR) literature. Providing a flexible and efficient way of dealing with such an issue is of paramount importance in medical domains, where many patient parameters are often collected in the form of time series. In the past, we have developed a framework for retrieving cases with time series features, relying on Temporal Abstractions. With respect to more classical (mathematical) approaches, our framework provides significant advantages. In particular, multi-level abstraction mechanisms and proper indexing techniques allow for flexible query issuing, and for efficient and interactive query answering. In this paper, we present an extension to such a framework, aimed at supporting sub-series matching as well. Indeed, sub-series retrieval may be crucial in medical applications, when the whole time series evolution is sometimes not of interest, while critical patterns to be search for are only “local”. Moreover, their relative order, but not their precise location in time, may be known, and an interactive search, at different abstraction levels, may be of great help for the medical decision maker. The framework is currently being applied to the hemodialysis domain.