Temporal representation and reasoning in medicine

Procedure prediction from symbolic Electronic Health Records via time intervals analytics

Prediction of medical events, such as clinical procedures, is essential for preventing disease, understanding disease mechanism, and increasing patient quality of care. Although longitudinal clinical data from Electronic Health Records provides opportunities to develop predictive models, the use of these data faces significant challenges. Primarily, while the data are longitudinal and represent thousands of conceptual events having duration, they are also sparse, complicating the application of traditional analysis approaches. Furthermore, the framework presented here takes advantage of the events duration and gaps. International standards for electronic healthcare data represent data elements, such as procedures, conditions, and drug exposures, using eras, or time intervals. Such eras contain both an event and a duration and enable the application of time intervals mining - a relatively new subfield of data mining. In this study, we present Maitreya, a framework for time intervals analytics in longitudinal clinical data. Maitreya discovers frequent time intervals related patterns (TIRPs), which we use as prognostic markers for modelling clinical events. We introduce three novel TIRP metrics that are normalized versions of the horizontal-support, that represents the number of TIRP instances per patient. We evaluate Maitreya on 28 frequent and clinically important procedures, using the three novel TIRP representation metrics in comparison to no temporal representation and previous TIRPs metrics. We also evaluate the epsilon value that makes Allen's relations more flexible with several settings of 30, 60, 90 and 180days in comparison to the default zero. For twenty-two of these procedures, the use of temporal patterns as predictors was superior to non-temporal features, and the use of the vertically normalized horizontal support metric to represent TIRPs as features was most effective. The use of the epsilon value with thirty days was slightly better than the zero.

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.