An Imbalanced Learning based MDR-TB Early Warning System.
As a man-made disease, multidrug-resistant tuberculosis (MDR-TB) is mainly caused by improper treatment programs and poor patient supervision, most of which could be prevented. According to the daily treatment and inspection records of tuberculosis (TB) cases, this study focuses on establishing a wa...
| Publicado en: | Journal of Medical Systems Vol. 40; no. 7; pp. 1 - 10 |
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| Autores principales: | , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jul2016
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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=115925375&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 115925375 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Jul2016 vid: 40 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 115925375 115925375 115925375 10.1007/s10916-016-0517-2 115925375 ppf: 1 ppct: 9 formats: fmt: @attributes: type: P tig: atl: An Imbalanced Learning based MDR-TB Early Warning System. aug: au: Li, Sheng Tang, Bo He, Haibo affil: School of Information and Safety Engineering, Zhongnan University of Economics & Law, Wuhan China sug: subj: Tuberculosis, Multidrug-Resistant Risk Factors Algorithms Human China Record Review Infant, Newborn Infant Child Adolescence Adult Middle Age Aged Aged, 80 and Over Female Male ROC Curve False Positive Results Regression T-Tests Descriptive Statistics Infant, Newborn: birth-1 month Infant: 1-23 months Child: 6-12 years Adolescent: 13-18 years Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Female Male ab: As a man-made disease, multidrug-resistant tuberculosis (MDR-TB) is mainly caused by improper treatment programs and poor patient supervision, most of which could be prevented. According to the daily treatment and inspection records of tuberculosis (TB) cases, this study focuses on establishing a warning system which could early evaluate the risk of TB patients converting to MDR-TB using machine learning methods. Different imbalanced sampling strategies and classification methods were compared due to the disparity between the number of TB cases and MDR-TB cases in historical data. The final results show that the relative optimal predictions results can be obtained by adopting CART-USBagg classification model in the first 90 days of half of a standardized treatment process. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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