Emerging medical informatics with case-based reasoning for aiding clinical decision in multi-agent system.
Identifieur interne : 000174 ( France/Analysis ); précédent : 000173; suivant : 000175Emerging medical informatics with case-based reasoning for aiding clinical decision in multi-agent system.
Auteurs : Ying Shen [France] ; Joël Colloc [France] ; Armelle Jacquet-Andrieu [France] ; Kai Lei [République populaire de Chine]Source :
- Journal of biomedical informatics [ 1532-0480 ] ; 2015.
English descriptors
- KwdEn :
- Algorithms, Artificial Intelligence, Computer Systems, Decision Making, Decision Support Systems, Clinical, Decision Support Techniques, Diagnosis, Computer-Assisted, Fuzzy Logic, Humans, Medical Informatics (methods), Models, Statistical, Probability, Problem Solving, Prognosis, Stomach Neoplasms (diagnosis), Stomach Neoplasms (therapy).
- MESH :
- diagnosis : Stomach Neoplasms.
- methods : Medical Informatics.
- therapy : Stomach Neoplasms.
- Algorithms, Artificial Intelligence, Computer Systems, Decision Making, Decision Support Systems, Clinical, Decision Support Techniques, Diagnosis, Computer-Assisted, Fuzzy Logic, Humans, Models, Statistical, Probability, Problem Solving, Prognosis.
Abstract
This research aims to depict the methodological steps and tools about the combined operation of case-based reasoning (CBR) and multi-agent system (MAS) to expose the ontological application in the field of clinical decision support. The multi-agent architecture works for the consideration of the whole cycle of clinical decision-making adaptable to many medical aspects such as the diagnosis, prognosis, treatment, therapeutic monitoring of gastric cancer. In the multi-agent architecture, the ontological agent type employs the domain knowledge to ease the extraction of similar clinical cases and provide treatment suggestions to patients and physicians. Ontological agent is used for the extension of domain hierarchy and the interpretation of input requests. Case-based reasoning memorizes and restores experience data for solving similar problems, with the help of matching approach and defined interfaces of ontologies. A typical case is developed to illustrate the implementation of the knowledge acquisition and restitution of medical experts.
DOI: 10.1016/j.jbi.2015.06.012
PubMed: 26133480
Affiliations:
- France, République populaire de Chine
- Guangdong, Haute-Normandie, Région Normandie, Île-de-France
- Le Havre, Nanterre, Paris, Shenzhen
- Université du Havre
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pubmed:26133480Le document en format XML
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Electronic address: ying.shen@u-paris10.fr.</nlm:affiliation><country xml:lang="fr">France</country><wicri:regionArea>Peking University Shenzhen Graduate School - Institute of Big Data Technologies, School of Electronics and Computer Engineering (SECE), Shenzhen Key Lab for Cloud Computing Technology & Applications, PKU Shenzhen Graduate School, 518055 Shenzhen, PR China; Université Paris Ouest (UMR CNRS 7114) - MoDyCo, 200 avenue de la République, 92000 Nanterre</wicri:regionArea><placeName><region type="region" nuts="2">Île-de-France</region><settlement type="city">Nanterre</settlement></placeName></affiliation></author><author><name sortKey="Colloc, Joel" sort="Colloc, Joel" uniqKey="Colloc J" first="Joël" last="Colloc">Joël Colloc</name><affiliation wicri:level="4"><nlm:affiliation>Université du Havre (UMR CNRS CIRTAI/IDEES 6266), 25 Rue Philippe Lebon, 76086 Le Havre Cedex, France. 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The multi-agent architecture works for the consideration of the whole cycle of clinical decision-making adaptable to many medical aspects such as the diagnosis, prognosis, treatment, therapeutic monitoring of gastric cancer. In the multi-agent architecture, the ontological agent type employs the domain knowledge to ease the extraction of similar clinical cases and provide treatment suggestions to patients and physicians. Ontological agent is used for the extension of domain hierarchy and the interpretation of input requests. Case-based reasoning memorizes and restores experience data for solving similar problems, with the help of matching approach and defined interfaces of ontologies. A typical case is developed to illustrate the implementation of the knowledge acquisition and restitution of medical experts.</div></front></TEI><affiliations><list><country><li>France</li><li>République populaire de Chine</li></country><region><li>Guangdong</li><li>Haute-Normandie</li><li>Région Normandie</li><li>Île-de-France</li></region><settlement><li>Le Havre</li><li>Nanterre</li><li>Paris</li><li>Shenzhen</li></settlement><orgName><li>Université du Havre</li></orgName></list><tree><country name="France"><region name="Île-de-France"><name sortKey="Shen, Ying" sort="Shen, Ying" uniqKey="Shen Y" first="Ying" last="Shen">Ying Shen</name></region><name sortKey="Colloc, Joel" sort="Colloc, Joel" uniqKey="Colloc J" first="Joël" last="Colloc">Joël Colloc</name><name sortKey="Jacquet Andrieu, Armelle" sort="Jacquet Andrieu, Armelle" uniqKey="Jacquet Andrieu A" first="Armelle" last="Jacquet-Andrieu">Armelle Jacquet-Andrieu</name></country><country name="République populaire de Chine"><region name="Guangdong"><name sortKey="Lei, Kai" sort="Lei, Kai" uniqKey="Lei K" first="Kai" last="Lei">Kai Lei</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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