Querying temporal clinical databases on granular trends

This paper focuses on the identification of temporal trends involving different granularities in clinical databases, where data are temporal in nature: for example, while follow-up visit data are usually stored at the granularity of working days, queries on these data could require to consider trends either at the granularity of months (“find patients who had an increase of systolic blood pressure within a single month”) or at the granularity of weeks (“find patients who had steady states of diastolic blood pressure for more than 3 weeks”).Representing and reasoning properly on temporal clinical data at different granularities are important both to guarantee the efficacy and the quality of care processes and to detect emergency situations. Temporal sequences of data acquired during a care process provide a significant source of information not only to search for a particular value or an event at a specific time, but also to detect some clinically-relevant patterns for temporal data.We propose a general framework for the description and management of temporal trends by considering specific temporal features with respect to the chosen time granularity. Temporal aspects of data are considered within temporal relational databases, first formally by using a temporal extension of the relational calculus, and then by showing how to map these relational expressions to plain SQL queries. Throughout the paper we consider the clinical domain of hemodialysis, where several parameters are periodically sampled during every session.

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► We propose a framework considering granular temporal trends of clinical data.
► Temporal clinical data (hemodialysis data) are considered at different granularities.
► We propose a taxonomy for multi-granular temporal clinical trends.
► Queries detect trends over granules.
► Temporal queries are expressed by a temporal relational calculus and mapped to SQL.