Comput. Math. Methods Medicine最新文献_第2页

Identifying Methamphetamine Dependence Using Regional Homogeneity in BOLD Signals 利用BOLD信号的区域同质性识别甲基苯丙胺依赖

Comput. Math. Methods Medicine Pub Date : 2020-05-28 DOI: 10.1155/2020/3267949

Hufei Yu, Shucai Huang, Xiaojie Zhang, Qiuping Huang, Jun Liu, Hong-xian Chen, Yan Tang

{"title":"Identifying Methamphetamine Dependence Using Regional Homogeneity in BOLD Signals","authors":"Hufei Yu, Shucai Huang, Xiaojie Zhang, Qiuping Huang, Jun Liu, Hong-xian Chen, Yan Tang","doi":"10.1155/2020/3267949","DOIUrl":"https://doi.org/10.1155/2020/3267949","url":null,"abstract":"Methamphetamine is a highly addictive drug of abuse, which will cause a series of abnormal consequences mentally and physically. This paper is aimed at studying whether the abnormalities of regional homogeneity (ReHo) could be effective features to distinguish individuals with methamphetamine dependence (MAD) from control subjects using machine-learning methods. We made use of resting-state fMRI to measure the regional homogeneity of 41 individuals with MAD and 42 age- and sex-matched control subjects and found that compared with control subjects, individuals with MAD have lower ReHo values in the right medial superior frontal gyrus but higher ReHo values in the right temporal inferior fusiform. In addition, AdaBoost classifier, a pretty effective ensemble learning of machine learning, was employed to classify individuals with MAD from control subjects with abnormal ReHo values. By utilizing the leave-one-out cross-validation method, we got the accuracy more than 84.3%, which means we can almost distinguish individuals with MAD from the control subjects in ReHo values via machine-learning approaches. In a word, our research results suggested that the AdaBoost classifier-neuroimaging approach may be a promising way to find whether a person has been addicted to methamphetamine, and also, this paper shows that resting-state fMRI should be considered as a biomarker, a noninvasive and effective assistant tool for evaluating MAD.","PeriodicalId":182719,"journal":{"name":"Comput. Math. Methods Medicine","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133860750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 14

Bayesian Estimation of Gumbel Type-II Distribution under Type-II Censoring with Medical Applications ii型审查下Gumbel ii型分布的贝叶斯估计及其医学应用

Comput. Math. Methods Medicine Pub Date : 2020-03-26 DOI: 10.1155/2020/1876073

Kamran Abbas, Z. Hussain, Noreen Rashid, Amjad Ali, M. Taj, S. Khan, Sadaf Manzoor, U. Khalil, Dost Muhammad Khan

{"title":"Bayesian Estimation of Gumbel Type-II Distribution under Type-II Censoring with Medical Applications","authors":"Kamran Abbas, Z. Hussain, Noreen Rashid, Amjad Ali, M. Taj, S. Khan, Sadaf Manzoor, U. Khalil, Dost Muhammad Khan","doi":"10.1155/2020/1876073","DOIUrl":"https://doi.org/10.1155/2020/1876073","url":null,"abstract":"The time to event or survival time usually follows certain skewed probability distributions. These distributions encounter vital role using the Bayesian framework to analyze and project the maximum life expectancy in order to inform decision-making. The Bayesian method provides a flexible framework for monitoring the randomized clinical trials to update what is already known using prior information about specific phenomena under uncertainty. Additionally, medical practitioners can use the Bayesian estimators to measure the probability of time until tumor recurrence, time until cardiovascular death, and time until AIDS for HIV patients by considering the prior information. However, in clinical trials and medical studies, censoring is present when an exact event occurrence time is not known. The present study aims to estimate the parameters of Gumbel type-II distribution based on the type-II censored data using the Bayesian framework. The Bayesian estimators cannot be obtained in explicit forms, and therefore we use Lindley’s approximation based on noninformative prior and various loss functions such as squared error loss function, general entropy loss function, and LINEX (linear exponential) loss function. The maximum likelihood and Bayesian estimators are compared in terms of mean squared error by using the simulation study. Furthermore, two data sets about remission times (in months) of bladder cancer patients and survival times in weeks of 61 patients with inoperable adenocarcinoma of the lung are analyzed for illustration purposes.","PeriodicalId":182719,"journal":{"name":"Comput. Math. Methods Medicine","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131773525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 21

Mathematical Analysis of the Effects of Controls on the Transmission Dynamics of Anthrax in Both Animal and Human Populations 控制措施对炭疽热在动物和人群中传播动态影响的数学分析

Comput. Math. Methods Medicine Pub Date : 2020-02-18 DOI: 10.1155/2020/1581358

Elijah B. Baloba, Baba Seidu, C. S. Bornaa

引用次数: 8

A Spring-Dashpot System for Modelling Lung Tumour Motion in Radiotherapy 一种模拟放射治疗中肺肿瘤运动的弹簧-阻尼系统

Comput. Math. Methods Medicine Pub Date : 2010-03-01 DOI: 10.1080/17486700802616534

Phillip Lawrence Wilson, Juergen Meyer

引用次数: 7

Web-StrabNet: A Web-Based Expert System for the Differential Diagnosis of Vertical Strabismus (Squint) Web-StrabNet:基于web的垂直斜视鉴别诊断专家系统

Comput. Math. Methods Medicine Pub Date : 2010-01-08 DOI: 10.1080/17486700903010157

A. Fisher, S. P. Lake, I. Cunningham, A. Chandna

引用次数: 4

Stability Analysis of a Model of Atherogenesis: An Energy Estimate Approach II 动脉粥样硬化模型的稳定性分析:一种能量估计方法2

Comput. Math. Methods Medicine Pub Date : 2008-04-15 DOI: 10.1080/17486700802713430

A. Ibragimov, C. McNeal, L. Ritter, J. Walton

引用次数: 11

Letter from the Editors 编辑的信

Comput. Math. Methods Medicine Pub Date : 1900-01-01 DOI: 10.1080/17486701003665718

引用次数: 0

To Determine Pivotal Genes Driven by Methylated DNA in Obstructive Sleep Apnea Hypopnea Syndrome 确定阻塞性睡眠呼吸暂停低通气综合征中甲基化DNA驱动的关键基因

Comput. Math. Methods Medicine Pub Date : 1900-01-01 DOI: 10.1155/2021/5520325

Yan Li, Yajuan Zhang

引用次数: 1

A Modified Stochastic Gompertz Model for Tumour Cell Growth 肿瘤细胞生长的改进随机Gompertz模型

Comput. Math. Methods Medicine Pub Date : 1900-01-01 DOI: 10.1080/17486700802545543

Edward Chi-Fai Lo

引用次数: 28

Corrigendum: Modelling of Peripheral Fluid Accumulation after a Crystalloid Bolus in Female Volunteers - A Mathematical Study 更正:女性志愿者服用晶体丸后外周液体积聚的建模-一项数学研究

Comput. Math. Methods Medicine Pub Date : 1900-01-01 DOI: 10.1080/1748670X.2010.534901

Peter Rodhe, D. Drobin, R. Hahn, B. Wennberg, C. Lindahl, F. Sjöstrand, C. Svensen

引用次数: 1