2018 2nd International Conference on Biomedical Engineering (IBIOMED)最新文献

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The Effect of Inaccuracy of MLC Leaf Position against Dose Distribution on VMAT Delivery Technique using EGSnrc Monte Carlo Simulation 基于EGSnrc蒙特卡罗模拟的MLC叶片位置对剂量分布不准确对VMAT给药技术的影响

2018 2nd International Conference on Biomedical Engineering (IBIOMED) Pub Date : 2018-07-01 DOI: 10.1109/IBIOMED.2018.8534813

I. G. E. Dirgayussa, S. Yani, D. Pratama, M. F. Rhani, R. Soh, F. Haryanto

{"title":"The Effect of Inaccuracy of MLC Leaf Position against Dose Distribution on VMAT Delivery Technique using EGSnrc Monte Carlo Simulation","authors":"I. G. E. Dirgayussa, S. Yani, D. Pratama, M. F. Rhani, R. Soh, F. Haryanto","doi":"10.1109/IBIOMED.2018.8534813","DOIUrl":"https://doi.org/10.1109/IBIOMED.2018.8534813","url":null,"abstract":"A Monte Carlo simulation on VMAT delivery technique has implemented to investigate the effect of inaccuracy of MLC leaf position between the plan and the actual of MLC leaf position. The study aim to compare the dose distributions between these two parameters. In general, these studies divided into two stages, commissioning of head Linac and simulation of the VMAT delivery technique. At the commissioning stage, Monte Carlo simulation on PDD and dose profile for field size 6 × 6 cm2, 10 × 10 cm2 and 20 × 20 cm2 was validating by experimental data. Commissioning results showed a good agreement between the simulation and experimental data. The 6.4 MeV initial incident electrons were used with an average deviation less than 5% for all field size. In the VMAT delivery technique simulation stage, an analysis of DynaLog file using homemade MATLAB program is used and generated the input files for DOSXYZnrc. One dimension of dose profile analyzed at some point sampling to determine differences in the dose distribution in the cylinder acrylic phantom. DynaLog analysis on the data file showed that the leaf error position is less than 1 mm is 97.1% of the total leaf position and no leaf error position is more than 2 mm. The doses distribution error was discovered less than 2% at isocenter. This study showed there is no significant error on doses distribution due to <1% inaccuracy of MLC leaf position. The Monte Carlo simulation was done using 24.7 billion particles of the phase space history file with a standard deviation of Monte Carlo calculations by 31.66%.","PeriodicalId":217196,"journal":{"name":"2018 2nd International Conference on Biomedical Engineering (IBIOMED)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126505758","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: 1

Propensity Score Stratification Using Support Vector Machine in HIV AIDS Case 基于支持向量机的倾向评分分层在艾滋病病例中的应用

2018 2nd International Conference on Biomedical Engineering (IBIOMED) Pub Date : 2018-07-01 DOI: 10.1109/IBIOMED.2018.8534805

Ernawati, B. Otok, Sutikno

{"title":"Propensity Score Stratification Using Support Vector Machine in HIV AIDS Case","authors":"Ernawati, B. Otok, Sutikno","doi":"10.1109/IBIOMED.2018.8534805","DOIUrl":"https://doi.org/10.1109/IBIOMED.2018.8534805","url":null,"abstract":"Many observational studies applied in the field of health, but Randomized Controlled Trials (RCT) is not always can be applied because it is directly related to human life. Therefore, a method is needed to solve the problem of bias as the effect of non-random observation and unbalanced covariates using propensity score (PS), it is Propensity Score Stratification (PSS). The purpose of PSS is to obtain a strata group that balance on each covariate. The PSS estimation of this research is using support vector machine (SVM). The case used in this research is opportunistic infection of HIV AIDS at Grati Health Center in Pasuruan district with the number of respondents are 150 patients. In the case of opportunistic infections HIV AIDS found that giving ARV therapy becomes confounding variable.The highest accuracy of PSS SVM on strata is 4, that is 64%. Estimation of treatment effects (ATE) gave results that the variable of ARV therapy is a variable that influence the opportunistic infections (Y) in HIV AIDS patients. The number of strata that reduce the largest bias is in the strata of 4 with the percent bias reduction (PBR) is 37.168% with the smallest standard error value is 0.075 and ATE value is 0.516.","PeriodicalId":217196,"journal":{"name":"2018 2nd International Conference on Biomedical Engineering (IBIOMED)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127399725","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: 0

Combining of Genetic Algorithm and Multiple Linear Regression in Breast Cancer’s Drug Design 遗传算法与多元线性回归在乳腺癌药物设计中的结合

2018 2nd International Conference on Biomedical Engineering (IBIOMED) Pub Date : 2018-07-01 DOI: 10.1109/IBIOMED.2018.8534783

A. S. Devi, G. Hertono, D. Sarwinda, T. Siswantining

{"title":"Combining of Genetic Algorithm and Multiple Linear Regression in Breast Cancer’s Drug Design","authors":"A. S. Devi, G. Hertono, D. Sarwinda, T. Siswantining","doi":"10.1109/IBIOMED.2018.8534783","DOIUrl":"https://doi.org/10.1109/IBIOMED.2018.8534783","url":null,"abstract":"Breast cancer is the first cause of death by cancer in women. Even so, men could have breast cancer. In the treatment of breast cancer there are surgery, radiation therapy and systemic therapy which treatments using drugs. WHO has listed thirty cytotoxic and anticancer drugs to prevent and reduce breast cancer risk. Researchers have been trying to find other drugs to help people with breast cancer. Thus, drug design becomes more important in discovering new potential drugs to treat breast cancer. In this study, we proposed multiple linear regression (MLR) approach using quantitative structure activity relationship (QSAR) method for modelling drug design of breast cancer. Because the data are obtained from public protein bank have lower number of compounds than the number of features, it failed the assumptions of MLR analysis and led to multicollinearity. QSAR model appeared uncertain when multicollinearity arise. We implemented genetic algorithm (GA) to resolve multicollinearity. GA acted as a feature selector to obtain the most significant features and helped getting the most fitted QSAR model. The experimental result shows that combining of GA and MLR can be implemented in breast cancer's drug design with r-sq gt 0.38.","PeriodicalId":217196,"journal":{"name":"2018 2nd International Conference on Biomedical Engineering (IBIOMED)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125891422","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: 0

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