%0 Conference Proceedings
%T Guiding Supervised Learning by Bio-Ontologies in Medical Data Analysis
%+ George Mason University [Fairfax]
%A Wojtusiak, Janusz
%A Min, Hua
%A Elashkar, Eman
%A Mobahi, Hedyeh
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B 4th IFIP International Workshop on Artificial Intelligence for Knowledge Management (AI4KM)
%C New York, NY, United States
%Y Eunika Mercier-Laurent
%Y Danielle Boulanger
%I Springer International Publishing
%3 Artificial Intelligence for Knowledge Management
%V AICT-518
%P 1-18
%8 2016-07-09
%D 2016
%R 10.1007/978-3-319-92928-6_1
%K Supervised machine learning
%K Biomedical ontologies
%K UMLS
%Z Computer Science [cs]Conference papers
%X Ontologies are popular way of representing knowledge and semantics of data in medical and health fields. Surprisingly, few machine learning methods allow for encoding semantics of data and even fewer allow for using ontologies to guide learning process. This paper discusses the use of data semantics and ontologies in health and medical applications of supervised learning, and particularly describes how the Unified Medical Language System (UMLS) is used within AQ21 rule learning software. Presented concepts are illustrated using two applications based on distinctly different types of data and methodological issues.
%G English
%Z TC 12
%Z WG 12.6
%2 https://inria.hal.science/hal-01950012/document
%2 https://inria.hal.science/hal-01950012/file/469211_1_En_1_Chapter.pdf
%L hal-01950012
%U https://inria.hal.science/hal-01950012
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-WG12-6
%~ IFIP-TC12
%~ IFIP-AI4KM
%~ IFIP-AICT-518