From Texts to Structured Documents: The Case of Health Practice Guidelines

This paper describes a system capable of semi-automatically filling an XML template from free texts in the clinical domain (practice guidelines). The XML template includes semantic information not explicitly encoded in the text (pairs of conditions and actions/recommendations). Therefore, there is a need to compute the exact scope of conditions over text sequences expressing the required actions. We present in this paper the rules developed for this task. We show that the system yields good performance when applied to the analysis of French practice guidelines

Matrix and Stimulus Sample Sizes in the Weighted MDS Model: Empirical Metric Recovery Functions

The only guidelines for sample size that exist in the multidimensional scaling (MDS) literature are a set of heuristic "rules-of-thumb" that have failed to live up to Young's (1970) goal of finding func tional relationships between sample size and metric recovery. This paper develops answers to two im portant sample-size questions in nonmetric weight ed MDS settings, both of which are extensions of work reported in MacCallum and Cornelius (1977): (1) are the sample size requirements for number of stimuli and number of matrices compensatory? and (2) what type of functional relationships exist between the number of matrices and metric recov ery ? The graphs developed to answer the second question illustrate how such functional relation ships can be defined empirically in a wide range of MDS and other complicated nonlinear models.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

A Personalization Environment for Multi-Version Clinical Guidelines

In this work, we introduce a personalization environment for the representation and efficient management of multi-version clinical guidelines. The environment is composed of an XML repository accessed through a personalization engine, which uses temporal perspective, patient profile and context information to reconstruct a guideline version tailored to a specific use case. In particular, we apply and extend to clinical guidelines solutions we previously developed for norm texts in the legal domain, and show how multi-version representation capabilities and personalization query facilities can be added to their management

"Kayseri Gazi paşa İlkokulu"

In this paper, we present GLARE, a domain-independent system for acquiring, representing and executing clinical guidelines. GLARE is characterized by the adoption of Artificial Intelligence (AI) techniques at different levels in the definition and implementation of the system. First of all, a high-level and user-friendly knowledge representation language has been designed, providing a set of representation primitives. Second, a user-friendly acquisition tool has been designed and implemented, on the basis of the knowledge representation formalism. The acquisition tool provides various forms of help for the expert physicians, including different levels of syntactic and semantic tests in order to check the well-formedness of the guidelines being acquired. Third, a tool for executing guidelines on a specific patient has been made available. The execution module provides a hypothetical reasoning facility, to support physicians in the comparison of alternative diagnostic and/or therapeutic strategies. Moreover, advanced and extended AI techniques for temporal reasoning and temporal consistency checking are used both in the acquisition and in the execution phase. The GLARE approach has been successfully tested on clinical guidelines in different domains, including bladder cancer, reflux esophagitis, and heart failure