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].

Temporal representation and reasoning in medicine: research directions and challenges

Objective: The main aim of this paper is to propose and discuss promising directions of research in the field of temporal representation and reasoning in medicine, taking into account the recent scientific literature and challenging issues of current interest as viewed from the different research perspectives of the authors of the paper. Background: Temporal representation and reasoning in medicine is a well-known field of research in the medical as well as computer science community. It encompasses several topics, such as summarizing data from temporal clinical databases, reasoning on temporal clinical data for therapeutic assessments, and modeling uncertainty in clinical knowledge and data. It is also related to several medical tasks, such as monitoring intensive care patients, providing treatments for chronic patients, as well as planning and scheduling clinical routine activities within complex healthcare organizations. Methodology: The authors jointly identified significant research areas based on their importance as for temporal representation and reasoning issues; the subjects were considered to be promising topics of future activity. Every subject was addressed in detail by one or two authors and then discussed with the entire team to achieve a consensus about future fields of research. Results: We identified and focused on four research areas, namely (i) fuzzy logic, time, and medicine, (ii) temporal reasoning and data mining, (iii) health information systems, business processes, and time, and (iv) temporal clinical databases. For every area, we first highlighted a few basic notions that would permit any reader-including those who are unfamiliar with the topic-to understand the main goals. We then discuss interesting and promising directions of research, taking into account the recent literature and underlining the yet unresolved medical/clinical issues that deserve further scientific investigation. The considered research areas are by no means disjointed, because they share common theoretical and methodological features. Moreover, subjects of imminent interest in medicine are represented in many of the fields considered. Conclusions: We propose and discuss promising subjects of future research that deserve investigation to develop software systems that will properly manage the multifaceted temporal aspects of information and knowledge encountered by physicians during their clinical work. As the subjects of research have resulted from merging the different perspectives of the authors involved in this study, we hope the paper will succeed in stimulating discussion and multidisciplinary work in the described fields of researc

Which kind of knowledge is suitable for redesigning hospital logistic processes?

A knowledge management perspective is rarely used to model a process. Using the cognitive perspective on knowledge management in which we start our analysis with events and knowledge (bottom-up) instead of with processes and units (top-down), we propose a new approach for redesigning hospital logistic processes. To increase the care efficiency of multi-disciplinary patients, tailored knowledge in content and type that supports the reorganization of care should be provided. We discuss the advantages of several techniques in providing robust knowledge about the logistic hospital process by employing electronic patient records (EPR's) and diagnosis treatment combinations (DTC's)

Finding and explaining optimal treatments

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Temporal representation and reasoning in medicine: Research directions and challenges

OBJECTIVE: The main aim of this paper is to propose and discuss promising directions of research in the field of temporal representation and reasoning in medicine, taking into account the recent scientific literature and challenging issues of current interest as viewed from the different research perspectives of the authors of the paper. BACKGROUND: Temporal representation and reasoning in medicine is a well-known field of research in the medical as well as computer science community. It encompasses several topics, such as summarizing data from temporal clinical databases, reasoning on temporal clinical data for therapeutic assessments, and modeling uncertainty in clinical knowledge and data. It is also related to several medical tasks, such as monitoring intensive care patients, providing treatments for chronic patients, as well as planning and scheduling clinical routine activities within complex healthcare organizations. METHODOLOGY: The authors jointly identified significant research areas based on their importance as for temporal representation and reasoning issues; the subjects were considered to be promising topics of future activity. Every subject was addressed in detail by one or two authors and then discussed with the entire team to achieve a consensus about future fields of research. RESULTS: We identified and focused on four research areas, namely (i) fuzzy logic, time, and medicine, (ii) temporal reasoning and data mining, (iii) health information systems, business processes, and time, and (iv) temporal clinical databases. For every area, we first highlighted a few basic notions that would permit any reader--including those who are unfamiliar with the topic--to understand the main goals. We then discuss interesting and promising directions of research, taking into account the recent literature and underlining the yet unresolved medical/clinical issues that deserve further scientific investigation. The considered research areas are by no means disjointed, because they share common theoretical and methodological features. Moreover, subjects of imminent interest in medicine are represented in many of the fields considered. CONCLUSIONS: We propose and discuss promising subjects of future research that deserve investigation to develop software systems that will properly manage the multifaceted temporal aspects of information and knowledge encountered by physicians during their clinical work. As the subjects of research have resulted from merging the different perspectives of the authors involved in this study, we hope the paper will succeed in stimulating discussion and multidisciplinary work in the described fields of research

MeDIP real-time qPCR of maternal peripheral blood reliably identifies trisomy 21

Objective: To reevaluate the efficiency of the 12 differentially methylated regions (DMRs) used in the methylated DNA immunoprecipitation (MeDIP) real-time quantitative polymerase chain reaction (real-time qPCR) based approach, develop an improved version of the diagnostic formula and perform a larger validation study. Methods: Twelve selected DMRs were checked for copy number variants in the Database of Genomic Variants. The DMRs located within copy number variants were excluded from the analysis. One hundred and seventy-five maternal peripheral blood samples were used to reconstruct and evaluate the new diagnostic formula and for a larger-scale blinded validation study using MeDIP real-time qPCR. Results: Seven DMRs entered the final model of the prediction equation and a larger blinded validation study demonstrated 100% sensitivity and 99.2% specificity. No significant evidence for association was observed between cell free fetal DNA concentration and D value. Conclusion: The MeDIP real-time qPCR method for noninvasive prenatal diagnosis of trisomy 21 was confirmed and revalidated in 175 samples with satisfactory results demonstrating that it is accurate and reproducible. We are currently working towards simplification of the method to make it more robust and therefore easily, accurately, and rapidly reproduced and adopted by other laboratories. Nevertheless, larger scale validation studies are necessary before the MeDIP real-time qPCR-based method could be applied in clinical practice. © 2012 John Wiley & Sons, Ltd

'slick systems' and 'happy hackers': experience with group projects at UCL

In this paper, we describe our experience of group projects in the practical teaching of software engineering over a period of eight years. Our initial projects tended to be too challenging, and few groups managed to produce complete pieces of work. Recently, we have deliberately simplified tasks slightly, resulting in less frustration and better projects, so that students reap more benefits. Students learn about division of work, co-operation with others and scheduling of time. As students are required to provide assessments of other projects and of the contributions of members of their own project group, they are also encouraged to develop critical faculties. The staff effort involved in this method of teaching compares quite reasonably with traditional lectures

Abstracting Steady Qualitative Descriptions over Time from Noisy, High-Frequency Data

On-line monitoring at neonatal intensive care units produces high volumes of data. Numerous devices generate data at high frequency (one data set every second). Both, the high volume and the quite high error-rate of the data make it essential to reach at higher levels of description from suchraw data. These abstractions should improve the medical decision making. We will present a time-oriented data-abstraction method to derive steady qualitative descriptions from ..