Yuval Shahar

Monitoring, interpretation, and analysis of large amounts of timestamped clinical data are tasks that are at the core of tasks such as the management of chronic patients using clinical guidelines, the retrospective assessment of the quality of that application, or the related task of clinical research by learning new knowledge from the accumulating data. I briefly describe several conceptual and computational architectures developed over the past 20 years, mostly by my research teams at Stanford and Ben Gurion universities, for knowledge-based performance of these tasks, and highlight the complex and interesting relationships amongst them. Examples of such architectures include the IDAN goal-directed temporal-mediation and the Momentum data-driven monitoring architectures, both of which are based on the knowledge-based temporal-abstraction method; the KNAVE-II and VISITORS knowledge-based interactive-exploration frameworks for single and multiple longitudinal records; and the KarmaLego interval-based temporal data mining methodology. I point out the progression from individual-subject datainterpretation, monitoring, and therapy, to multiple-patient aggregate analysis and research, and finally to the discovery and learning of new knowledge. This progression can be viewed as a positive-feedback loop, in which new knowledge is brought back to bear upon both individual-patient management and on the learning of new and meaningful (temporal) associations.