Case Based Representation and Retrieval with Time Dependent Features

Abstract. The temporal dimension of the knowledge embedded in cases has often been neglected or oversimplified in Case Based Reasoning sys-tems. However, in several real world problems a case should capture the evolution of the observed phenomenon over time. To this end, we propose to represent temporal information at two levels: (1) at the case level, if some features describe parameters varying within a period of time (which corresponds to the case duration), and are therefore collected in the form of time series; (2) at the history level, if the evolution of the system can be reconstructed by retrieving temporally related cases. In this paper, we describe a framework for case representation and retrieval able to take into account the temporal dimension, and meant to be used in any time dependent domain. In particular, to support case retrieval, we provide an analysis of similarity-based time series retrieval techniques; to support history retrieval, we introduce possible ways to summarize the case content, together with the corresponding strategies for identifying similar instances in the knowledge base. A concrete ap-plication of our framework is represented by the system RHENE, which is briefly sketched here, and extensively described in [20].

Knowledge-Based Event Detection in Complex Time Series Data

Abstract. This paper describes an approach to the detection of events in complex, multi-channel, high frequency data. The example used is that of detecting the re-siting of a transcutaneous O 2/CO 2 probe on a baby in a neonatal intensive care unit (ICU) from the available monitor data. A software workbench has been developed which enables the expert clinician to display the data and to mark up features of interest. This knowledge is then used to define the parameters for a pattern matcher which runs over a set of intervals derived from the raw data by a new iterative interval merging algorithm. The approach has been tested on a set of 45 probe changes; the preliminary results are encouraging, with an accuracy of identification of 89% 1

Timing is Everything: Temporal Reasoning and Temporal Data Maintenance in Medicine

Both clinical management of patients and clinical research are essentially time-oriented endeavors. In this paper, I emphasize the crucial role of temporal-reasoning and temporal-maintenance tasks for modern medical information and decision support systems. Both tasks are important for management of clinical data, but the first is often approached mainly through artificial-intelligence methodologies, while the other is usually investigated by the database community. However, both tasks require careful consideration of common theoretical issues, such as the structure of time. In addition, common to both of these research areas are tasks such as temporal abstraction and management of variable temporal granularity. Finally, both tasks are highly relevant for applications such as patient monitoring, support to application of therapy guidelines, assessment of the quality of guideline application, and visualization and exploration of time-oriented biomedical data. I propose that integration of the two areas should be a major research and development goal. I demonstrate one integration approach by presenting a new architecture, a temporal mediator, which combines temporal reasoning and temporal maintenance, and integrates the management of clinical databases and medical knowledge bases. I present and discuss examples of using the temporal mediator for several of the application areas mentioned. I conclude by reemphasizing the importance of effective knowledge representation, knowledge reuse, and knowledge sharing methods to medical decision support systems in general, and to time-oriented systems in particular