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bayesian model

در نشریات گروه پزشکی
تکرار جستجوی کلیدواژه bayesian model در مقالات مجلات علمی
  • Azadeh Ghazanfari, Afshin Fayyaz Movaghar*
    Background and Purpose

    Genomic selection is used to select candidates for breeding programs for organisms. In this study, we use the Bayesian model averaging (BMA) method for genomic selection by considering the skewed error distributions.

    Materials and Methods

    In this study, we apply the BMA method to linear regression models with skew-normal and skew-t distributions to determine the best subset of predictors. Occam’s window and Markov-Chain Monte Carlo model composition (MC3) were used to determine the best model and its uncertainty. The Rice SNP-seek database was used to obtain real data, which included 152 single nucleotide polymorphisms (SNPs) with 6 phenotypes.

    Results

    Numerical studies on simulated and real data showed that, although Occam’s window ran faster than the MC3 method, the latter method suggested better linear models for the data with both skew-normal and skew-t error distributions.

    Conclusion

    The MC3 method performs better than Occam’s window in identifying the linear models with greater accuracy when dealing with skewed error distributions.

    Keywords: Genomic Selection, Single Nucleotide Polymorphism (Snps), Bayesian Model
  • Fatemeh Pourmotahari, Seyyed Mohammad Tabatabaei, Nasrin Borumandnia, Naghmeh Khadembashi, Keyvan Olazadeh, Hamid Alavimajd*
    Introduction

    Parkinson disease is a neurodegenerative disease that disrupts functional brain networks. Many neurodegenerative disorders are associated with changes in brain communication patterns. Resting-state functional connectivity studies can distinguish the topological structure of Parkinson patients from healthy individuals by analyzing patterns between different regions of the brain. Accordingly, the present study aimed to determine the brain topological features and functional connectivity in patients with Parkinson disease, using a Bayesian approach. 

    Methods

    The data of this study were downloaded from the open neuro site. These data include resting-state functional magnetic resonance imaging (rs-fMRI) of 11 healthy individuals and 11 Parkinson patients with mean ages of 64.36 and 63.73, respectively. An advanced nonparametric Bayesian model was used to evaluate topological characteristics, including clustering of brain regions and correlation coefficient of the clusters. The significance of functional relationships based on each edge between the two groups was examined through false discovery rate (FDR) and network-based statistics (NBS) methods. 

    Results

    Brain connectivity results showed a major difference in terms of the number of regions in each cluster and the correlation coefficient between the patient and healthy groups. The largest clusters in the patient and control groups were 26 and 53 regions, respectively, with clustering correlation values of 0.36 and 0.26. Although there are 15 common areas across the two clusters, the intensity of the functional relationship between these areas was different in the two groups. Moreover, using NBS and FDR methods, no significant difference was observed for each edge between the patient and healthy groups (P>0.05). 

    Conclusion

    The results of this study show a different topological configuration of the brain network between the patient and healthy groups, indicating changes in the functional relationship between a set of areas of the brain.

    Keywords: Parkinson disease, Functional Brain imaging, fMRI, Bayesian model
  • Soheil Hassanipour, Mojtaba Sepandi, Hadiseh Rabiei, Mahdi Malakoutikhah, Gholamhossein Pourtaghi*
    Background and Objectives

    Occupational accidents impose high costs on organizations annually. This study aimed at investigating the factors affecting military work‑related accidents using artificial neural network (ANN) and Bayesian models.

    Materials and Methods

    This study was a cross‑sectional survey in a military unit that examined all occupational accidents recorded during 2011–2018. First, we collected the data of the accidents using the accident database in the inspection sector of the Department of Health and the Medical Commission of the Armed Forces. ANN, Bayesian, and logistic regression models were used to analyze the data.

    Results

    The results of the type of accidents showed that 219 cases of sport accidents (32.8%), 125 cases fall from height (18.7%), and 104 cases of driving accidents (15.6%) were the most common accidents. Based on the results of multivariate regression, accident variables due to fighting (odds ratio [OR] =17.21), injury to the body or back (OR = 122.55), and multiple injuries (OR = 25.72) were considered as influential and significant factors. The ANNs results showed that the highest importance factor was the injury to the body or back, multiple injuries, age, fighting, and finally, driving accident. Furthermore, the Bayesian model showed that the most important factors affecting the death consequence due to accidents were related to injuries to the body or back (OR = 276.23), multiple injuries (OR = 54.98), and accidents due to conflict (OR = 33.69).

    Conclusion

    The findings show that the most important factors affecting the death consequence due to accidents in the military are the injury to the whole body, multiple injuries, age, fighting accident, and driving accident. The ANN and Bayesian models have provided more accurate information than logistic regression based on the obtained results.

    Keywords: Artificial neural networks, Bayesian model, military, occupational accidents
  • Mohamad Amin Pourhoseingholi, Hadis Najafimehr, Amir Kavousi, Leila Pasharavesh, Binazir Khanabadi
    Aim

    The aim of this study was to estimate the standardized incidence rate (SIR) and also the relative risk (RR) of colorectal cancer (CRC) in Iran and to determine the distribution of CRC risk in a map after adjusting socioeconomic risk factors.

    Background

    The growth of CRC incidence rate in Iran is a major public health problem and identifying high-risk regions is essential for further intervention.

    Methods

    For this cross-sectional study, all CRC cases that occurred in 30 Iranian provinces between 2005 and 2008 were collected according to the International Classification of Diseases (ICD-10). In addition, socioeconomic information was extracted from statistical center of Iran. Bayesian and Poison regression models were fitted to identify significant covariates. For RR estimating, the spatial analysis using GIS technique was carried out.

    Results

    The Bayesian method with increasing precision of the parameter estimates had a better fit. According to spatial model, East Azerbaijan province had a high (11.14) and South Khorasan province had a low (0.22) risk of CRC in the period of study. SIR for the male population was 1.92 ± 3.25, and for the female population it was 1.85 ± 3.37.

    Conclusion

    There is a non-uniform spatial pattern of CRC risk in Iran. According to the results, North, Northwest and some parts of West and Central provinces of Iran are identified as high-risk areas; thus, it is recommended that health policymakers, especially in these areas, have more intervention measures. Further studies are needed to map the RR adjusted for nutrition factors.

    Keywords: Colorectal Cancer, Relative risk, Bayesian model, Poisson regression, Spatial analysis
  • Vahid Ahmadipanah mehrabadi, Akbar Hassanzadeh, Behzad Mahaki *
    Background
    Gastrointestinal cancers make for nearly half fatal cancers with colorectal and stomach cancers’ being listed among the ten most common in Iran. This research aims to determine the spatial pattern and temporal trend of death risk due to colorectal and stomach cancers among provinces of Iran and estimate the effect of shared and specifc components as surrogates of risk factors for the aforementioned cancers on changes of death due to the cancers over time and place.
    Methods
    In this ecological study, the data regarding death causes in colorectal and stomach cancers during 2006–2011 were obtained from the death registration system of the Iranian Ministry of Health. The estimation of relative risk (RR) of death due to the target cancers was performed applying Bayesian spatiotemporal shared component (SC) model in OpenBUGS software.
    Results
    North‑Western provinces ranked frst regarding stomach cancer RR of death (RR >1.75). Furthermore, some North‑Western and central provinces had the highest RR of death due to colorectal cancer (RR >1.5). The SC surrogating the risk factor shared between both cancers had the most effect in Northern, North‑Western and western provinces, and the least effect in Southern and South‑Eastern ones.
    Conclusions
    North and North‑West of Iran found to be the high‑risk area for death due to both stomach and colorectal cancers and South‑East and South provinces shown to have the lowest RR. The obtained results can be illuminating to health resource allocation to the health policymakers.
    Keywords: Bayesian model, colorectal cancer, disease mapping, model, shared component, stomachcancer
  • Hadis Najafimehr, Sara Ashtari, Mohammad Amin Pourhoseingholi *, Luca Busani
    Aim
    The aim of this study was to identify the esophageal cancer (EC) high risk regions to evaluate changes of relative risks (RRs) for both genders by time in Iranian provinces.
    Background
    EC is one of the public health problems in Iran. In spite of this fact, there is not comprehensive study estimating RRs across the Iranian provinces.
    Methods
    In this cross-sectional study the data for EC cases were extracted from Ministry of Health and Medical Education (MOHME) including 30 provinces from 2004 to 2010. For estimating the model parameters, we used Bayesian approach by regarding spatial correlations of adjacent provinces.
    Results
    The Northern half of Iran has high risk and other half has low risk. During the time, the range of RRs has decreased for both gender and also the dispersion of EC is decreasing for women but nearly is fixed for men.
    Conclusion
    While RR has declined during the study, focusing on the Northern half of Iran as high risk regions is a considerable fact for policymakers.
    Keywords: Esophageal cancer, Relative risks, Bayesian model, Iran
  • نجف زارع، سمیه خدارحمی، عباس رضاییان زاده
    مقدمه و اهداف
    سرطان پستان یکی از شایع ترین سرطآن ها در بین زنان بوده و پس از سرطان ریه دومین علت مرگ ومیر را به خود اختصاص می دهد. هدف از این مطالعه، استفاده از مدل های بیز برای بررسی اثر عوامل پیش آگهی روی بقای زنان مبتلا به سرطان پستان در جنوب ایران پس از عمل جراحی است.
    روش کار
    طی سال های 85-1380، داده های مربوط به 1192 بیمار مبتلا به سرطان پستان در مرکز تحقیقات سرطان بیمارستان نمازی جمع آوری شد. از این تعداد، داده های کامل مربوط به 1148 بیمار به طور کامل ثبت شد. داده ها توسط دو روش بیز پارامتری و کاکس بیزی و با در نظر گرفتن 05/0 به عنوان سطح معنی داری با استفاده از نرم افزار WinBUGS نسخه 14 تجزیه و تحلیل شدند.
    یافته ها
    میانگین سنی بیماران (سن در زمان تشخیص) در این مطالعه 47 سال به دست آمد. در تحلیل تک متغیره کاکس ارتباط معنی داری بین متغیرهای سیگار کشیدن (009/0=P)، متاستاز به استخوان ( 01/0=P) ، تعداد گره های لنفاوی درگیر
    (001/0=P)، درجه بدخیمی تومور (نوع یک: well-differentiated، نوع دو: moderately-differentiated، نوع سه: poorly-differentiated) (001/0=P)، روش جراحی ( 015/0=P)، وضع اقتصادی ( 025/0=P) و اندازه ی تومور (001/0=P) با مرگ ومیر مشاهده گردید و در ادامه با برازش مدل های بیز، تنها متغیرهای اندازه ی تومور، درجه ی بدخیمی تومور و تعداد گره های لنفاوی درگیر از نظر معنی دار شد.
    نتیجه گیری
    با توجه به نتایج این مطالعه، متغیرهای وابسته به ویژگی بالینی بیماری در مرگ ومیر تاثیر اصلی را داشتند
    کلید واژگان: بقا، سرطان پستان، مدل بیز، درجه بدخیمی، گره های لنفاوی
    N. Zare, S. Khodarahmi, A. Rezaianzadeh
    Background And Objectives
    Breast cancer is one of the most common cancers among women and is the second main cause of death after lung cancer. The objective of this study was to use the Bayes model to analyze the prognostic effects on the survival of the women with breast cancer after surgery in the south of Iran.
    Methods
    The date was collected 1192 women who had breast cancer in Namazi Hospital Research Center between 2001 and 2006. The complete information of only 1148 of them was registered. Parametric Bayes and Bayes Cox methods were used. Considering 0.05 as the level of significance, the data analysis was done using the WinBUGS14 software.
    Results
    The mean age of the patients (at the time of diagnosis) was 47 years in this study. Cox one-variable analysis showed a significant relationship between survival and smoking (P=0.009), bone metastasis (P=0.01), the number of lymph nodes (P=0.001), the tumoral level of malignancy (P=0.001), the surgical method (P=0.015), financial status (P=0.025), and the tumor size (P=0.001). By fitting Bayes models the variables tumor size, level of malignancy and number of lymph nodes were significant.
    Conclusion
    The results showed that clinicopathological features of cancer had a significant role in the survival of the patients.
    Keywords: Survival, Breast cancer, Bayesian model, Level of malignancy, Lymph nodes
  • معصومه آشوری راد *، رسول باغبانی خضرلو
    زمینه و هدف
    سیگنال الکتروکاردیوگرام (ECG) نمایشی گرافیکی از فعالیت قلبی است. پردازش و تحلیل تغییرات مورفولوژیکی آن می تواند به تشخیص بصری بسیاری از بیماری های قلبی کمک کند. با این وجود، انواع نویز واغتشاش در سیگنال ECG تشخیص بصری و استخراج ویژگی از آن را به شدت تحت تاثیر قرار می دهد. هدف از این پژوهش، حذف نویزهای مختلف سیگنال ECG و بهبود کیفیت آن می باشد.
    مواد و روش ها
    در این پژوهش، فیلتر تطبیقی کالمن بر اساس مدل بیزین استنتاج شد. با در نظر گرفتن ساده سازی های صورت گرفته و توزیع گوسی برای نویز اندازه گیری، روابط ریاضی پیچیده به روابط ساده تبدیل شد و در نتیجه پیاده سازی آسان گشت.
    یافته ها
    در این مقاله، نسبت سیگنال به نویز (SNR) با استفاده از طراحی فیلتر تطبیقی کالمن به میزان 21.46dB افزایش یافت. فیلتر تطبیقی کالمن با استنتاج از چارچوب بیزین قادر است تغییرات دینامیکی سیگنال ECG را با استفاده از تخمین ماتریس کوواریانس نویز اندازه گیری مدل سازی کند.
    نتیجه گیری
    برخلاف فیلترهای کالمنی که سیگنال ECG را بر اساس توابع پارامتری مدل سازی می کنند، فیلتر تطبیقی کالمن ارائه شده در این مقاله، ثبت های ECG واقعی را برای مدل سازی به کارگرفته است. توابع پارامتری که بتوانند تغییرات دینامیکی ECG را مدل سازی کنند نیازمند تعداد زیادی توابع تحلیلی هستند و این باعث کندشدن فرایند فیلترینگ می گردد. اما فیلتر تطبیقی کالمن ارائه شده در این پژوهش از سرعت بالایی برخوردار بوده و می تواند در کاربردهای زمان واقعی به کار گرفته شود.
    کلید واژگان: فیلتر تطبیقی کالمن، مدل بیزین، الکتروکاردیوگرام، تخمین نویز
    Masoomeh Ashoorirad *, Rasool Baghbani Khezerloo
    Background
    Electrocardiogram signal (ECG) is a graphical representation of the heart activity. Processing and analysis of these morphological changes can result in visual diagnosing some cardiac diseases. However، various types of noises and disturbances in ECG influence the visual recognition and feature extraction from it. The aim of this research is to eliminate different noises from ECG and to enhance its quality.
    Materials And Methods
    In this study، an adaptive Kalman filter is developed by using Bayesian model. Considering simplification and Gaussian distribution for measurement noise، complicated mathematical equations were converted to simple relations and therefore implementation was simplified.
    Results
    In this paper، by designing an adaptive Kalman filter، the signal to noise ratio (SNR) has increased to 21. 46dB. Adaptive Kalman filter based on Beyesian framework could model dynamic variations of ECG signal by estimating covariance matrix for measurement noise.
    Conclusion
    In despite of Kalman filters that use parametric functions to model ECG signal، the adaptive Kalman filter introduced in this paper uses real ECG records for modeling. Parametric functions which could model dynamic variations of ECG need a lot of analytical functions and this decreases the time of filtering process but the adaptive Kalman filter proposed in this research has a high speed and could be used in real time applications.
    Keywords: Adaptive kalman filter, Bayesian model, Electrocardiogram, Noise estimation
نکته
  • نتایج بر اساس تاریخ انتشار مرتب شده‌اند.
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