Jean Moulin University Lyon 3

About: Jean Moulin University Lyon 3 is a education organization based out in Lyon, Rhône-Alpes, France. It is known for research contribution in the topics: Computer science & Geology. The organization has 343 authors who have published 572 publications receiving 3175 citations.

Information Structure Representation And Extraction From A Corpus Of Patient Data, Using An Ontology

Abstract: We propose a methodology to model the information structure for its extraction from any medical text. We experiment this extraction in a corpus that represents the information system of a specific professional activity in the hospital pharmacy. the information structure represent how the meaning of a sentence is specified by the constraints of the information flow. But the information structure is systematically recognized and interpreted in the context of a text: it’s also the last object of the information system. Then we consider the text as a contextual frame to model the recognition and the extraction of the information structure. A text can’t be considered only as a linguistic object in a professional and information context: it’s an implemented (or externalised following situated and distributed cognition) ontology. The updated text articulates the ontology of the patient body and the referential dimension of the information. The model of the information structure presupposes we know what are the constraints of the information system on the symbolic entities (in a way to distinguish the information structure to any sentence description). In a way to determine these constraints, we propose to model the information process by the information flow: we represent in this way how any fact in the body of the patient is symbolised, conveyed and represented into a text. The information flow characterizes only the constraints of the information on the linguistic entities and structures. But the information is linguistically a referential semantic object: it’s the representation at distance of a new fact in the world in the frame of a text that accepts this information. Then the model of At last, The information flow allows the articulation of an ontology and a semantic precisely on the question of the information structure. We unify the model of the information structure by the definition of five primitives. A sign representation allows both the characterisation of the structure and of each of its components.

Reply on RC1

Abstract: Snow avalanches are the predominant hazards in winter in high elevation mountains. They cause damage to both humans and assets but cannot be accurately predicted. Until now, only local maps to estimate snow avalanche risk have been produced. Here we show how remote sensing can accurately inventory large avalanches every year at a basin scale using a 32-yr snow index derived from Landsat satellite archives. This Snow Avalanche Frequency Estimation (SAFE) built in an open-access Google Engine script maps snow hazard frequency and targets vulnerable areas in remote regions of Afghanistan, one of the most data-limited areas worldwide. SAFE correctly detected of the actual avalanches identified on Google Earth and in the field (Probability of Detection 0.77 and Positive Predictive Value 0.96). A total of 810,000 large avalanches occurred since 1990 within an area of 28,500 km2 with a mean frequency of 0.88 avalanches/km2yr−1, damaging villages and blocking roads and streams. Snow avalanche frequency did not significantly change with time, but a northeast shift of these hazards was evident. SAFE is the first robust model that can be used worldwide and is capable of filling data voids on snow avalanche impacts in inaccessible regions.