יובל שחר

The analysis of clinicaL data accumulating over time from multipLe sources, regarding a group of patients, can lead to muLtiple insights. In particular, such an analysis enables: (a) the discovery of temporal patterns repeating above a certain threshold frequency, thus essentially creating clusters of different behaviors over time of various patient sub-groups; and (b) detection amongst the temporal patterns, some of which, together with additional patient data, such as demographic data, might be able to predict future cLinically significant outcomes, such as renal dysfunction in the case of a diabetes patient. The analysis of temporal data is even more efficient when it includes not only time-stamped, point-based raw data, but also time intervals (periods) during which certain context-sensitive, abstract interpretations of the data hold, such as a period of moderate anemia instead of a series of hemoglobin values; or a degradation in liver functions, instead of a series of different enzyme values. Such an interpretation naturally requires an explicit representation of the medical knowledge involved, in a medical knowledge base, in a manner that supports automated computational tools. In this survey, we briefly introduce several of the innovative computational methods developed in our research center, for the purpose of the multivariate analysis of time-oriented clinical data. We mainly demonstrate the use of tools for exploration and knowledge discovery which are intended for use by a cLinicaL user at the point of care, and by cLinical researchers or heaLthcare policy makers. *