Imbalanced target prediction with pattern discovery on clinical data repositories.
Background: Clinical data repositories (CDR) have great potential to improve outcome prediction and risk modeling. However, most clinical studies require careful study design, dedicated data collection efforts, and sophisticated modeling techniques before a hypothesis can be tested. We aim to bridge...
| Publicado en: | BMC Medical Informatics & Decision Making Vol. 17; pp. 1 - 13 |
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| Autores principales: | , , , , , , , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
BioMed Central
4/20/2017
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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=122656489&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 122656489 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726947 1CI0 jtl: BMC Medical Informatics & Decision Making issn: 14726947 maglogo: N pubinfo: dt: 4/20/2017 vid: 17 pid: 24147 pub: BioMed Central artinfo: ui: 122656489 122656489 NLM28427384 122656489 10.1186/s12911-017-0443-3 NLM28427384 122656489 ppf: 1 ppct: 12 formats: tig: atl: Imbalanced target prediction with pattern discovery on clinical data repositories. aug: au: Tak-Ming Chan Yuxi Li Choo-Chiap Chiau Jane Zhu Jie Jiang Yong Huo Chan, Tak-Ming Li, Yuxi Chiau, Choo-Chiap Zhu, Jane Jiang, Jie Huo, Yong affil: Philips Research China - Health Systems, China, Philips Innovation Campus Shanghai, No. 1 Building, 10, Lane 888, Tian Lin Road, Shanghai 200233, China sug: subj: Data Mining Methods Computer Simulation Information Science Methods Databases Administration Algorithms Health Information Systems Administration Forecasting Human ab: Background: Clinical data repositories (CDR) have great potential to improve outcome prediction and risk modeling. However, most clinical studies require careful study design, dedicated data collection efforts, and sophisticated modeling techniques before a hypothesis can be tested. We aim to bridge this gap, so that clinical domain users can perform first-hand prediction on existing repository data without complicated handling, and obtain insightful patterns of imbalanced targets for a formal study before it is conducted. We specifically target for interpretability for domain users where the model can be conveniently explained and applied in clinical practice.Methods: We propose an interpretable pattern model which is noise (missing) tolerant for practice data. To address the challenge of imbalanced targets of interest in clinical research, e.g., deaths less than a few percent, the geometric mean of sensitivity and specificity (G-mean) optimization criterion is employed, with which a simple but effective heuristic algorithm is developed.Results: We compared pattern discovery to clinically interpretable methods on two retrospective clinical datasets. They contain 14.9% deaths in 1 year in the thoracic dataset and 9.1% deaths in the cardiac dataset, respectively. In spite of the imbalance challenge shown on other methods, pattern discovery consistently shows competitive cross-validated prediction performance. Compared to logistic regression, Naïve Bayes, and decision tree, pattern discovery achieves statistically significant (p-values < 0.01, Wilcoxon signed rank test) favorable averaged testing G-means and F1-scores (harmonic mean of precision and sensitivity). Without requiring sophisticated technical processing of data and tweaking, the prediction performance of pattern discovery is consistently comparable to the best achievable performance.Conclusions: Pattern discovery has demonstrated to be robust and valuable for target prediction on existing clinical data repositories with imbalance and noise. The prediction results and interpretable patterns can provide insights in an agile and inexpensive way for the potential formal studies. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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