A Conditional Probability Model to Predict the Mortality in Patients With Breast Cancer: A Bayesian Network Analysis

A Conditional Probability Model to Predict the Mortality in Patients With Breast Cancer: A Bayesian Network Analysis


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نویسندگان: زینب ایرجی , محمد اصغری جعفرآبادی , توحید جعفری کشکی , رویا دولت خواه

کلمات کلیدی: Bayesian network Survival Breast cancer Conditional probability IPCW

نشریه: 1876 , 0 , 0 , 2020

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نویسنده ثبت کننده مقاله محمد اصغری جعفرآبادی
مرحله جاری مقاله تایید نهایی
دانشکده/مرکز مربوطه دانشکده بهداشت
کد مقاله 73721
عنوان فارسی مقاله A Conditional Probability Model to Predict the Mortality in Patients With Breast Cancer: A Bayesian Network Analysis
عنوان لاتین مقاله A Conditional Probability Model to Predict the Mortality in Patients With Breast Cancer: A Bayesian Network Analysis
ناشر 4
آیا مقاله از طرح تحقیقاتی و یا منتورشیپ استخراج شده است؟ خیر
عنوان نشریه (خارج از لیست فوق)
نوع مقاله Original Article
نحوه ایندکس شدن مقاله ایندکس شده سطح یک – ISI - Web of Science
آدرس لینک مقاله/ همایش در شبکه اینترنت

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Background The aim of this study was to compute the event rate of patients with breast cancer (BC) using Bayesian network (BN) structure. Method Data for 1,154 patients newly diagnosed with BC were recruited in this study during 2007 and 2016 in Iran. The database was linked to the regional death registration system and active follow-up was performed by referring to hospital information system or calling the patients. BN structure with inverse probability of censoring weighting (IPCW) approach was used to assess the relationship between event rate and underlying risk factors. Results The median (25th, 75th percentiles) of patients’ survival time was 46.8 (32.6, 69.3) months. There were 217 (18.8%) deaths from BC by the end of the study. The optimal BN structure (Akaike Information Criteria = −8743.66 and Bayesian Information Criteria = −8790.80) indicated that being male (conditional probability [CP] = 0.316), age >50 (CP = 0.215), higher grades (CP = 0.301) and lower survival times (CP = 0.566) had higher event rate. Also lobular carcinoma (CP = 0.157) and ductal carcinoma (CP = 0.178) type of morphology had lower event rate while other types (CP = 0.316) had higher. Conclusions The BN structure in which time was as a mediator of predictors-event relationship could be presented as the optimal tool to compute the event rate of BC. The findings could be used to identify the high risk patients and recommend for health policy making, prevention and planning for decrease the mortality in patients with BC.

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نویسنده نفر چندم مقاله
زینب ایرجیاول
محمد اصغری جعفرآبادیدوم
توحید جعفری کشکیسوم
رویا دولت خواهچهارم

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