Scenario recognition for temporal reasoning in medical domains
| العنوان: | Scenario recognition for temporal reasoning in medical domains |
|---|---|
| المؤلفون: | Michel Dojat, Nicolas Ramaux, Dominique Fontaine |
| المساهمون: | Résonance magnétique nucléaire bioclinique, Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de la Santé et de la Recherche Médicale (INSERM), Heuristique et Diagnostic des Systèmes Complexes [Compiègne] (Heudiasyc), Université de Technologie de Compiègne (UTC)-Centre National de la Recherche Scientifique (CNRS), Coll, Dojat, Michel |
| المصدر: | Artificial Intelligence in Medicine Artificial Intelligence in Medicine, 1998, 14 (1-2), pp.139-55 Artificial Intelligence in Medicine, Elsevier, 1998, 14 (1-2), pp.139-55 |
| بيانات النشر: | HAL CCSD, 1998. |
| سنة النشر: | 1998 |
| مصطلحات موضوعية: | Time Factors, MESH: Blood Volume, Remote patient monitoring, Computer science, Cardiac Output, Low, Medicine (miscellaneous), MESH: Monitoring, Physiologic, 02 engineering and technology, computer.software_genre, Pattern Recognition, Automated, Task (project management), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Heart Rate, MESH: Airway Obstruction, 0202 electrical engineering, electronic engineering, information engineering, Edema, MESH: Pattern Recognition, Automated, Session (computer science), MESH: Respiration, Artificial, MESH: Heart Rate, MESH: Cardiac Output, Low, Blood Volume, Respiration, Process (computing), MESH: Suction, MESH: Edema, MESH: Respiration Disorders, Déjà vu, 020201 artificial intelligence & image processing, [SDV.IB]Life Sciences [q-bio]/Bioengineering, Data mining, Algorithms, [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Matching (statistics), MESH: Algorithms, Suction, Machine learning, Decision Support Techniques, MESH: Neural Networks (Computer), Artificial Intelligence, 020204 information systems, Intubation, Intratracheal, Humans, MESH: Artificial Intelligence, Set (psychology), Monitoring, Physiologic, MESH: Respiration, [SDV.IB] Life Sciences [q-bio]/Bioengineering, MESH: Humans, MESH: Intubation, Intratracheal, business.industry, MESH: Time Factors, MESH: Decision Support Techniques, Respiration Disorders, Respiration, Artificial, Airway Obstruction, Constraint (information theory), Neural Networks, Computer, Artificial intelligence, business, computer |
| الوصف: | International audience; The recognition of high level clinical scenes is fundamental in patient monitoring. In this paper, we propose a technique for recognizing a session, i.e. the clinical process evolution, by comparison against a predetermined set of scenarios, i.e. the possible behaviors for this process. We use temporal constraint networks to represent both scenario and session. Specific operations on networks are then applied to perform the recognition task. An index of temporal proximity is introduced to quantify the degree of matching between two temporal networks in order to select the best scenario fitting a session. We explore the application of our technique, implemented in the Déjà Vu system, to the recognition of typical medical scenarios with both precise and imprecise temporal information. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 0933-3657 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b5a8c12349e500cf634738e59f2c611c https://www.hal.inserm.fr/inserm-00402424/document |
| حقوق: | EMBARGO |
| رقم الأكسشن: | edsair.doi.dedup.....b5a8c12349e500cf634738e59f2c611c |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 09333657 |
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