Multiple Imputation based Clustering Validation (MIV) for Big Longitudinal Trial Data with Missing Values in eHealth.
Web-delivered trials are an important component in eHealth services. These trials, mostly behavior-based, generate big heterogeneous data that are longitudinal, high dimensional with missing values. Unsupervised learning methods have been widely applied in this area, however, validating the optimal...
| Publicado en: | Journal of Medical Systems Vol. 40; no. 6; pp. 1 - 10 |
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| Autores principales: | , , |
| Formato: | algorithm equations & formulas tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2016
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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=115925350&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 115925350 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Jun2016 vid: 40 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 115925350 115925350 115925350 10.1007/s10916-016-0499-0 115925350 ppf: 1 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Multiple Imputation based Clustering Validation (MIV) for Big Longitudinal Trial Data with Missing Values in eHealth. aug: au: Zhang, Zhaoyang Fang, Hua Wang, Honggang affil: Department of Quantitative Health Science, University of Massachusetts Medical School, Worcester 01655 USA sug: subj: Data Analytics Algorithms Clinical Trials Human Smoking Cessation Computer Simulation Funding Source ab: Web-delivered trials are an important component in eHealth services. These trials, mostly behavior-based, generate big heterogeneous data that are longitudinal, high dimensional with missing values. Unsupervised learning methods have been widely applied in this area, however, validating the optimal number of clusters has been challenging. Built upon our multiple imputation (MI) based fuzzy clustering, MIfuzzy, we proposed a new multiple imputation based validation (MIV) framework and corresponding MIV algorithms for clustering big longitudinal eHealth data with missing values, more generally for fuzzy-logic based clustering methods. Specifically, we detect the optimal number of clusters by auto-searching and -synthesizing a suite of MI-based validation methods and indices, including conventional (bootstrap or cross-validation based) and emerging (modularity-based) validation indices for general clustering methods as well as the specific one (Xie and Beni) for fuzzy clustering. The MIV performance was demonstrated on a big longitudinal dataset from a real web-delivered trial and using simulation. The results indicate MI-based Xie and Beni index for fuzzy-clustering are more appropriate for detecting the optimal number of clusters for such complex data. The MIV concept and algorithms could be easily adapted to different types of clustering that could process big incomplete longitudinal trial data in eHealth services. pubtype: Academic Journal doctype: algorithm equations & formulas tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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