Title :
Semantic Data Types in Machine Learning from Healthcare Data
Author_Institution :
Machine Learning & Inference Lab., George Mason Univ., Fairfax, VA, USA
Abstract :
Healthcare is particularly rich in semantic information and background knowledge describing data. This paper discusses a number of semantic data types that can be found in healthcare data, presents how the semantics can be extracted from existing sources including the Unified Medical Language System (UMLS), discusses how the semantics can be used in both supervised and unsupervised learning, and presents an example rule learning system that implements several of these types. Results from three example applications in the healthcare domain are used to further exemplify semantic data types.
Keywords :
formal languages; health care; unsupervised learning; UMLS; background knowledge describing data; healthcare data; machine learning; rule learning system; semantic data types; semantic information; supervised learning; unified medical language system; unsupervised learning; Cognition; Compounds; Machine learning; Medical services; Semantics; Terminology; Unified modeling language; UMLS; healthcare; machine learning; semantic data types;
Conference_Titel :
Machine Learning and Applications (ICMLA), 2012 11th International Conference on
Conference_Location :
Boca Raton, FL
Print_ISBN :
978-1-4673-4651-1
DOI :
10.1109/ICMLA.2012.41