Literature DB >> 30119981

Data Mining and Endocrine Diseases: A New Way to Classify?

Juan Salazar1, Cristobal Espinoza2, Andres Mindiola3, Valmore Bermudez4.   

Abstract

Data mining consists of using large database analysis to detect patterns, relationships and models in order to describe (or even predict) the appearance of a future event; to accomplish this, it uses classification methods, rules of association, regression patterns, link and cluster analyses. Recently this approach has been used to propose a new diabetes mellitus classification, using information analysis techniques through which the selection bias minimally influences categorization, this new focus that includes data mining previously implemented to predict, identify biomarkers, complications, therapies, health policies, genetic and environmental effects of this disease; it could be generalized in the field of endocrinology, in the classification of other endocrine diseases.
Copyright © 2018 IMSS. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Classification; Data mining; Diabetes mellitus; Endocrine disease; Information analysis

Mesh:

Substances:

Year:  2018        PMID: 30119981     DOI: 10.1016/j.arcmed.2018.08.005

Source DB:  PubMed          Journal:  Arch Med Res        ISSN: 0188-4409            Impact factor:   2.235


  2 in total

1.  A survey on data mining techniques used in medicine.

Authors:  Saba Maleki Birjandi; Seyed Hossein Khasteh
Journal:  J Diabetes Metab Disord       Date:  2021-08-31

2.  Automated data extraction of electronic medical records: Validity of data mining to construct research databases for eligibility in gastroenterological clinical trials.

Authors:  Nora Joseph; Ida Lindblad; Sara Zaker; Sharareh Elfversson; Maria Albinzon; Øyvind Ødegård; Li Hantler; Per M Hellström
Journal:  Ups J Med Sci       Date:  2022-01-27       Impact factor: 2.384

  2 in total

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