A multi-lingual architecture for building a normalised conceptual representation from medical language.

The overall goal of MENELAS is to provide better access to the information contained in natural language patient discharge summaries (PDSs), through the design and implementation of a prototype able to analyse medical texts. The approach taken by MENELAS is based on the following key principles: (i) to maximise the usefulness of natural language analysis and the usability of its results, the output of natural language analysis must be a normalised conceptual representation of medical information; and (ii) to maximise the reuse of resources, language analysis should be domain-independent and conceptual representation should be language-independent. This paper discusses the results obtained and the issues raised when implementing these principles during the project

From text to knowledge: a unifying document-centered view of analyzed medical language.

Although medical language processing (MLP) has achieved some success, the actual use and dissemination of data extracted from free text by MLP systems is still very limited. We claim that the adoption of an 'enriched-document' paradigm (or 'document-centered' view) can help to address this issue. We present this paradigm and explain how it can be implemented, then discuss its expected benefits both for end-users and MLP researchers

[Patient-centered consultation of "good practice guidelines": OncoDoc, a decision support system for the management of breast cancer patients].

Beyond considerations of cost-effectiveness, clinical practice guidelines (CPG) can reduce practice variations and thus improve the quality of care. However, despite the proliferation of implemented CPG and their wide diffusion thanks to Internet-based technologies, physicians' compliance with formal standards is weak. Developed according to a document-based paradigm, OncoDoc proposes an original framework for implementing CPG. Domain knowledge has been encoded as a decision tree whose branches are both exclusive and exhaustive. This generic knowledge is operationalized at the point of care by the interactive building, through hypertextual navigation, of a patient-based clinical context leading to specific therapeutic recommendations. OncoDoc has first been applied to the management of breast cancer patients and demonstrated within a full-scale experimentation in a clinical setting a compliance of 80 per cent