Evaluating a normalized conceptual representation produced from natural language patient discharge summaries.

The Menelas project aimed to produce a normalized conceptual representation from natural language patient discharge summaries. Because of the complex and detailed nature of conceptual representations, evaluating the quality of output of such a system is difficult. We present the method designed to measure the quality of Menelas output, and its application to the state of the French Menelas prototype as of the end of the project. We examine this method in the framework recently proposed by Friedman and Hripcsak. We also propose two conditions which enable to reduce the evaluation preparation workload

Language-independent Automatic Acquisition of Morphological Knowledge from Synonym Pairs

INTRODUCTION Medical words exhibit a rich and productive morphology. In French or English, as well as in many other European languages, they are often formed using Greek or Latin roots and affixes. The decomposition of a word into its component morphemes is useful to get at its elementary meaning units. This is a key to more relevant and more principled semantic processing of medical utterances. In an even simpler way, this allows finer grained indexing of medical texts and terms, and potentially better accuracy for information retrieval and coding assistants 1 . Three types of morphological variations are classically distinguished: (i) inflection (e.g., plural form creation) creates variant forms of the same word; (ii) derivation adds affixes (prefixes or suffixes) around one root (e.g., to obtain the adjectival form of a noun); and<F7.63

Hospitexte: towards a document-based Hypertextual Electronic Medical Record

INTRODUCTION Documents are the core of medical reflection: in a Paperbased Medical Record (PMR), we found a lot (typically 150-200) of documents which give to the health-care professional board all the information necessary to follow up and treat the patient. These documents are of different natures (paper, image, etc) and come from different sources (laboratories, clinical departments, etc). Moreover, laboratory results come with textual reports which give important information about the exam. Assuming that we want to change the support of the PMR for an Electronic Medical record (EMR), few effective technologies may propose an appropriate solution. In a traditional approach which tends to formalize knowledge, a view of the EMR is that of a database which holds items of coded, or standardized, information. In a different way, effective technologies like hypertext offer an opportunity to develop new systems based on the concept of "document processing". Our claim is that if w