Computer-aided disease prediction system: development of application software with SAS component language.
Aims The intricacy of predictive models associated with prognosis and risk classification of disease often discourages medical personnel who are interested in this field. The aim of this study was therefore to develop a computer-aided disease prediction model underpinning a step-by-step statistics-g...
| Publicado en: | Journal of Evaluation in Clinical Practice Vol. 11; no. 2; pp. 139 - 160 |
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| Autores principales: | , , , , , , |
| Formato: | clinical trial pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Apr2005
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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=106621661&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 106621661 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13561294 EV1 jtl: Journal of Evaluation in Clinical Practice issn: 13561294 maglogo: Y pubinfo: dt: Apr2005 vid: 11 iid: 2 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 106621661 2005071037 10.1111/j.1365-2753.2005.00514.x NLM15813712 106621661 ppf: 139 ppct: 21 formats: tig: atl: Computer-aided disease prediction system: development of application software with SAS component language. aug: au: Chang C Kuo H Chang S Chang H Liou D Laszlo T Chen TH affil: Institute of Public Health, School of Medicine, National Yang-Ming University, Pei-Tou, Taipei, Taiwan sug: subj: Breast Neoplasms Prognosis Software Clinical Trials Consumer Satisfaction Cox Proportional Hazards Model Female Kaplan-Meier Estimator Logistic Regression Male Sweden Human Female Male ab: Aims The intricacy of predictive models associated with prognosis and risk classification of disease often discourages medical personnel who are interested in this field. The aim of this study was therefore to develop a computer-aided disease prediction model underpinning a step-by-step statistics-guided approach including five components: (1) data management; (2) exploratory analysis; (3) type of predictive model; (4) model verification; (5) interactive mode of disease prediction using SAS 8.02 Windows 2000 as a platform.Methods The application of this system was illustrated by using data from the Swedish Two-County Trial on breast cancer screening. The effects of tumour size, node status, and histological grade on breast cancer death using logistic regression model or survival models were predicted. A total of 20 questions were designed to exemplify the usefulness of each component. We also evaluated the system using a controlled randomized trial. Times to finish the above 20 questions were used as endpoint to evaluate the performance of the current system. User satisfaction with the current system such as easy to use, the efficiency of risk prediction, and the reduction of barrier to predictive model was also evaluated.Results The intervention group not only performed more efficiently than the control group but also satisfied with this application software.Conclusions The MD-DP-SOS system characterized by menu-driven style, comprehensiveness, accuracy and adequacy assessment, and interactive mode of disease prediction is helpful for medical personnel who are involved in disease prediction. pubtype: Academic Journal doctype: clinical trial pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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