Predicting postoperative complications of head and neck squamous cell carcinoma in elderly patients using random forest algorithm model.
Background: Head and Neck Squamous Cell Carcinoma (HNSCC) has a high incidence in elderly patients. The postoperative complications present great challenges within treatment and they're hard for early warning.Methods: Data from 525 patients diagnosed with HNSCC including a training set (n = 513) and...
| Publicado en: | BMC Medical Informatics & Decision Making Vol. 15; no. 1; pp. 44 - 45 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
BioMed Central
2015
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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=109745582&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109745582 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726947 1CI0 jtl: BMC Medical Informatics & Decision Making issn: 14726947 maglogo: N pubinfo: dt: 2015 vid: 15 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 109745582 NLM26054335 2013042218 10.1186/s12911-015-0165-3 NLM26054335 PMC4459053 109745582 ppf: 44 ppct: 1 formats: tig: atl: Predicting postoperative complications of head and neck squamous cell carcinoma in elderly patients using random forest algorithm model. aug: au: Chen, YiMing Cao, Wei Gao, XianChao Ong, HuiShan Ji, Tong sug: ab: Background: Head and Neck Squamous Cell Carcinoma (HNSCC) has a high incidence in elderly patients. The postoperative complications present great challenges within treatment and they're hard for early warning.Methods: Data from 525 patients diagnosed with HNSCC including a training set (n = 513) and an external testing set (n = 12) in our institution between 2006 and 2011 was collected. Variables involved are general demographic characteristics, complications, disease and treatment given. Five data mining algorithms were firstly exploited to construct predictive models in the training set. Subsequently, cross-validation was used to compare the different performance of these models and the best data mining algorithm model was then selected to perform the prediction in an external testing set.Results: Data from 513 patients (age > 60 y) with HNSCC in a training set was included while 44 variables were selected (P < 0.05). Five predictive models were constructed; the model with 44 variables based on the Random Forest algorithm demonstrated the best accuracy (89.084%) and the best AUC value (0.949). In an external testing set, the accuracy (83.333%) and the AUC value (0.781) were obtained by using the random forest algorithm model.Conclusions: Data mining should be a promising approach used for elderly patients with HNSCC to predict the probability of postoperative complications. Our results highlighted the potential of computational prediction of postoperative complications in elderly patients with HNSCC by using the random forest algorithm model. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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