Skip to Main content Skip to Navigation
Conference papers

Guiding Supervised Learning by Bio-Ontologies in Medical Data Analysis

Abstract : 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.
Document type :
Conference papers
Complete list of metadata

Cited literature [43 references]  Display  Hide  Download

https://hal.inria.fr/hal-01950012
Contributor : Hal Ifip <>
Submitted on : Monday, December 10, 2018 - 3:09:34 PM
Last modification on : Thursday, February 7, 2019 - 3:38:35 PM
Long-term archiving on: : Monday, March 11, 2019 - 2:41:13 PM

File

469211_1_En_1_Chapter.pdf
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Janusz Wojtusiak, Hua Min, Eman Elashkar, Hedyeh Mobahi. Guiding Supervised Learning by Bio-Ontologies in Medical Data Analysis. 4th IFIP International Workshop on Artificial Intelligence for Knowledge Management (AI4KM), Jul 2016, New York, NY, United States. pp.1-18, ⟨10.1007/978-3-319-92928-6_1⟩. ⟨hal-01950012⟩

Share

Metrics

Record views

120

Files downloads

7