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Ontology-Based Tools to Expedite Predictive Model Construction

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 ma...

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Detalles Bibliográficos
Autores principales: Haug, Peter, Holmen, John, Wu, Xinzi, Mynam, Kumar, Ebert, Matthew, Ferraro, Jeffrey
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Medical Informatics Association 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4419766/
https://www.ncbi.nlm.nih.gov/pubmed/25954574
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author Haug, Peter
Holmen, John
Wu, Xinzi
Mynam, Kumar
Ebert, Matthew
Ferraro, Jeffrey
author_facet Haug, Peter
Holmen, John
Wu, Xinzi
Mynam, Kumar
Ebert, Matthew
Ferraro, Jeffrey
author_sort Haug, Peter
collection PubMed
description 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.
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spelling pubmed-44197662015-05-07 Ontology-Based Tools to Expedite Predictive Model Construction Haug, Peter Holmen, John Wu, Xinzi Mynam, Kumar Ebert, Matthew Ferraro, Jeffrey AMIA Jt Summits Transl Sci Proc Articles 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. American Medical Informatics Association 2014-04-07 /pmc/articles/PMC4419766/ /pubmed/25954574 Text en ©2014 AMIA - All rights reserved. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose
spellingShingle Articles
Haug, Peter
Holmen, John
Wu, Xinzi
Mynam, Kumar
Ebert, Matthew
Ferraro, Jeffrey
Ontology-Based Tools to Expedite Predictive Model Construction
title Ontology-Based Tools to Expedite Predictive Model Construction
title_full Ontology-Based Tools to Expedite Predictive Model Construction
title_fullStr Ontology-Based Tools to Expedite Predictive Model Construction
title_full_unstemmed Ontology-Based Tools to Expedite Predictive Model Construction
title_short Ontology-Based Tools to Expedite Predictive Model Construction
title_sort ontology-based tools to expedite predictive model construction
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4419766/
https://www.ncbi.nlm.nih.gov/pubmed/25954574
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