Subpopulation-specific confidence designation for more informative biomedical classification.
Objective: Although classification algorithms are promising tools to support clinical diagnosis and treatment of disease, the usual implicit assumption underlying these algorithms, that all patients are homogeneous with respect to characteristics of interest, is unsatisfactory. The objective here is...
| Publicado en: | Artificial Intelligence in Medicine Vol. 58; no. 3; pp. 155 - 164 |
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| Autores principales: | , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Jul2013
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104189818&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104189818 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09333657 3HY jtl: Artificial Intelligence in Medicine issn: 09333657 maglogo: N pubinfo: dt: Jul2013 vid: 58 iid: 3 pid: 1004 pub: Elsevier B.V. artinfo: ui: 104189818 NLM23731649 2012178146 10.1016/j.artmed.2013.04.008 NLM23731649 PMC3727244 104189818 ppf: 155 ppct: 9 formats: tig: atl: Subpopulation-specific confidence designation for more informative biomedical classification. aug: au: Zhang, Chuanlei Kodell, Ralph L affil: Department of Applied Mathematics and Computer Science, Philander Smith College, 900 W. Daisy L. Gatson Bates Dr., Little Rock, AR 72202, United States. sug: subj: Algorithms Data Mining Methods Resource Databases Individualized Medicine Methods Artificial Intelligence Cluster Analysis Decision Support Techniques Diagnosis, Computer Assisted Diagnosis, Differential Gene Expression Profiling Methods Genes Genetic Screening Methods Genetics Genotype Human Information Science Phenotype Predictive Value of Tests Prognosis Reproducibility of Results Therapy, Computer Assisted ab: Objective: Although classification algorithms are promising tools to support clinical diagnosis and treatment of disease, the usual implicit assumption underlying these algorithms, that all patients are homogeneous with respect to characteristics of interest, is unsatisfactory. The objective here is to exploit the population heterogeneity reflected by characteristics that may not be apparent and thus not controlled, in order to differentiate levels of classification accuracy between subpopulations and further the goal of tailoring therapies on an individual basis.Methods and Materials: A new subpopulation-based confidence approach is developed in the context of a selective voting algorithm defined by an ensemble of convex-hull classifiers. Populations of training samples are divided into three subpopulations that are internally homogeneous, with different levels of predictivity. Two different distance measures are used to cluster training samples into subpopulations and assign test samples to these subpopulations.Results: Validation of the new approach's levels of confidence of classification is carried out using six publicly available datasets. Our approach demonstrates a positive correspondence between the predictivity designations derived from training samples and the classification accuracy of test samples. The average difference between highest- and lowest-confidence accuracies for the six datasets is 17.8%, with a minimum of 11.3% and a maximum of 24.1%.Conclusion: The classification accuracy increases as the designated confidence increases. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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