title: Ontology-based tools to expedite predictive model construction.
abstract: Large amounts of medical data are collected electronically during the course of caring for patients using modern medical information systems. This data presents an opportunity to develop clinically useful tools through data mining and observational research studies. However, the work necessary to make sense of this data and to integrate it into a research initiative can require substantial effort from medical experts as well as from experts in medical terminology, data extraction, and data analysis. This slows the process of medical research. To reduce the effort required for the construction of computable, diagnostic predictive models, we have developed a system that hybridizes a medical ontology with a large clinical data warehouse. Here we describe components of this system designed to automate the development of preliminary diagnostic models and to provide visual clues that can assist the researcher in planning for further analysis of the data behind these models.
authors.affiliation: Intermountain Healthcare, Salt Lake City, Utah ; University of Utah.
authors.forename: Peter
authors.initials: P
authors.lastname: Haug
authors.affiliation: Intermountain Healthcare, Salt Lake City, Utah.
authors.forename: John
authors.initials: J
authors.lastname: Holmen
authors.affiliation: Intermountain Healthcare, Salt Lake City, Utah.
authors.forename: Xinzi
authors.initials: X
authors.lastname: Wu
authors.affiliation: Intermountain Healthcare, Salt Lake City, Utah.
authors.forename: Kumar
authors.initials: K
authors.lastname: Mynam
authors.affiliation: Intermountain Healthcare, Salt Lake City, Utah.
authors.forename: Matthew
authors.initials: M
authors.lastname: Ebert
authors.affiliation: Intermountain Healthcare, Salt Lake City, Utah.
authors.forename: Jeffrey
authors.initials: J
authors.lastname: Ferraro