Search

Your search keyword '"Devignes MD"' showing total 3 results
Start Over Author "Devignes MD" Topic artificial intelligence
3 results on '"Devignes MD"'

1. Application of Artificial Intelligence to Gastroenterology and Hepatology.

Author

Le Berre C, Sandborn WJ, Aridhi S, Devignes MD, Fournier L, Smaïl-Tabbone M, Danese S, and Peyrin-Biroulet L

Subjects
Clinical Decision-Making methods, Decision Support Systems, Clinical, Decision Trees, Gastrointestinal Diseases mortality, Gastrointestinal Diseases therapy, Humans, Liver Diseases mortality, Liver Diseases therapy, Prognosis, Treatment Outcome, Artificial Intelligence, Diagnosis, Computer-Assisted methods, Gastroenterology methods, Gastrointestinal Diseases diagnosis, Liver Diseases diagnosis
Abstract

Since 2010, substantial progress has been made in artificial intelligence (AI) and its application to medicine. AI is explored in gastroenterology for endoscopic analysis of lesions, in detection of cancer, and to facilitate the analysis of inflammatory lesions or gastrointestinal bleeding during wireless capsule endoscopy. AI is also tested to assess liver fibrosis and to differentiate patients with pancreatic cancer from those with pancreatitis. AI might also be used to establish prognoses of patients or predict their response to treatments, based on multiple factors. We review the ways in which AI may help physicians make a diagnosis or establish a prognosis and discuss its limitations, knowing that further randomized controlled studies will be required before the approval of AI techniques by the health authorities., (Copyright © 2020 AGA Institute. Published by Elsevier Inc. All rights reserved.)

Published
2020
Full Text
View/download PDF

2. Integrative relational machine-learning for understanding drug side-effect profiles.

Author

Bresso E, Grisoni R, Marchetti G, Karaboga AS, Souchet M, Devignes MD, and Smaïl-Tabbone M

Subjects
Databases, Pharmaceutical, Decision Trees, Reproducibility of Results, Artificial Intelligence, Computational Biology methods, Drug-Related Side Effects and Adverse Reactions
Abstract

Background: Drug side effects represent a common reason for stopping drug development during clinical trials. Improving our ability to understand drug side effects is necessary to reduce attrition rates during drug development as well as the risk of discovering novel side effects in available drugs. Today, most investigations deal with isolated side effects and overlook possible redundancy and their frequent co-occurrence., Results: In this work, drug annotations are collected from SIDER and DrugBank databases. Terms describing individual side effects reported in SIDER are clustered with a semantic similarity measure into term clusters (TCs). Maximal frequent itemsets are extracted from the resulting drug x TC binary table, leading to the identification of what we call side-effect profiles (SEPs). A SEP is defined as the longest combination of TCs which are shared by a significant number of drugs. Frequent SEPs are explored on the basis of integrated drug and target descriptors using two machine learning methods: decision-trees and inductive-logic programming. Although both methods yield explicit models, inductive-logic programming method performs relational learning and is able to exploit not only drug properties but also background knowledge. Learning efficiency is evaluated by cross-validation and direct testing with new molecules. Comparison of the two machine-learning methods shows that the inductive-logic-programming method displays a greater sensitivity than decision trees and successfully exploit background knowledge such as functional annotations and pathways of drug targets, thereby producing rich and expressive rules. All models and theories are available on a dedicated web site., Conclusions: Side effect profiles covering significant number of drugs have been extracted from a drug ×side-effect association table. Integration of background knowledge concerning both chemical and biological spaces has been combined with a relational learning method for discovering rules which explicitly characterize drug-SEP associations. These rules are successfully used for predicting SEPs associated with new drugs.

Published
2013
Full Text
View/download PDF

3. Application of Artificial Intelligence to Gastroenterology and Hepatology

Author

Marie-Dominique Devignes, Catherine Le Berre, Laure Fournier, Laurent Peyrin-Biroulet, Malika Smaïl-Tabbone, Silvio Danese, Sabeur Aridhi, William J. Sandborn, Université de Paris - UFR Médecine Paris Centre [Santé] (UP Médecine Paris Centre), Université de Paris (UP), Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), Paris-Centre de Recherche Cardiovasculaire (PARCC (UMR_S 970/ U970)), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP), ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), Le Berre, C, Sandborn, Wj, Aridhi, S, Devignes, Md, Fournier, L, Smail-Tabbone, M, Danese, S, Peyrin-Biroulet, L, Centre hospitalier universitaire de Nantes (CHU Nantes), Nutrition-Génétique et Exposition aux Risques Environnementaux (NGERE), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Service d'Hépato-gastro-entérologie [CHRU Nancy], Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy), University of California [San Diego] (UC San Diego), University of California, Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Computational Algorithms for Protein Structures and Interactions (CAPSID), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Paris-Centre de Recherche Cardiovasculaire (PARCC - UMR-S U970), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM), Istituto Clinico Humanitas [Milan] (IRCCS Milan), Humanitas University [Milan] (Hunimed), Université de Lorraine (UL)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université Paris Descartes - Paris 5 (UPD5)-Hôpital Européen Georges Pompidou [APHP] (HEGP), Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Assistance publique - Hôpitaux de Paris (AP-HP) (APHP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Assistance publique - Hôpitaux de Paris (AP-HP) (APHP)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Lorraine (UL), University of California (UC), UFR Médecine [Santé] - Université Paris Cité (UFR Médecine UPCité), Université Paris Cité (UPCité), and Humanitas Clinical and Research Center [Rozzano, Milan, Italy]

Subjects
medicine.medical_specialty, Gastrointestinal bleeding, Gastrointestinal Diseases, Clinical Decision-Making, MEDLINE, Controlled studies, Gastroenterology, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], law.invention, 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Artificial Intelligence, Capsule endoscopy, law, Internal medicine, Pancreatic cancer, medicine, Humans, Diagnosis, Computer-Assisted, ComputingMilieux_MISCELLANEOUS, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Hepatology, business.industry, Liver Diseases, Decision Trees, Cancer, [SDV.MHEP.HEG]Life Sciences [q-bio]/Human health and pathology/Hépatology and Gastroenterology, Decision Support Systems, Clinical, Prognosis, medicine.disease, 3. Good health, Treatment Outcome, 030220 oncology & carcinogenesis, Pancreatitis, 030211 gastroenterology & hepatology, Artificial intelligence, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], business
Abstract

International audience; Since 2010, substantial progress has been made in artificial intelligence (AI) and its application to medicine. AI is explored in gastroenterology for endoscopic analysis of lesions, in detection of cancer, and to facilitate the analysis of inflammatory lesions or gastrointestinal bleeding during wireless capsule endoscopy. AI is also tested to assess liver fibrosis and to differentiate patients with pancreatic cancer from those with pancreatitis. AI might also be used to establish prognoses of patients or predict their response to treatments, based on multiple factors. We review the ways in which AI may help physicians make a diagnosis or establish a prognosis and discuss its limitations, knowing that further randomized controlled studies will be required before the approval of AI techniques by the health authorities.

Published
2020
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results