There are many conditions that can affect the tumor destruction during ablation of tumor. The purpose of this article is to study which factors influenced the effect of electroporation in the Abdominal surgery. Our research focuses on ablation size and temperature. Five factors were taken into account including pulse intensity (U), electrode diameter (D), the location of the electrode from the center of the tumor (R), electrode length (L) and number of electrode (M). In the study, the changes of the electric field in the ablation area were monitored by numerical modeling and simulation of tumor. At the same time, the degree of tumor ablation was evaluated according to temperature threshold and electric field threshold. A number of experiments were conducted to verify the effect of various parameters on the outcome of tumor treatment. The uncertainty analysis of the experimental results showed that U and L had great influence on the ablation volume, while D, R and M had a small influence. In addition, U, R, and M have significant effects on the maximum temperature, while D and L only have a small impact. Finally, the uncertainty is quantified by using the polynomial chaos expansion to establish a mathematical model between them.
{"title":"Numerical modeling and Uncertainty analysis of irreversible electroporation in liver tumors","authors":"Yuwei Jiang, Lei Yang","doi":"10.1145/3448748.3448761","DOIUrl":"https://doi.org/10.1145/3448748.3448761","url":null,"abstract":"There are many conditions that can affect the tumor destruction during ablation of tumor. The purpose of this article is to study which factors influenced the effect of electroporation in the Abdominal surgery. Our research focuses on ablation size and temperature. Five factors were taken into account including pulse intensity (U), electrode diameter (D), the location of the electrode from the center of the tumor (R), electrode length (L) and number of electrode (M). In the study, the changes of the electric field in the ablation area were monitored by numerical modeling and simulation of tumor. At the same time, the degree of tumor ablation was evaluated according to temperature threshold and electric field threshold. A number of experiments were conducted to verify the effect of various parameters on the outcome of tumor treatment. The uncertainty analysis of the experimental results showed that U and L had great influence on the ablation volume, while D, R and M had a small influence. In addition, U, R, and M have significant effects on the maximum temperature, while D and L only have a small impact. Finally, the uncertainty is quantified by using the polynomial chaos expansion to establish a mathematical model between them.","PeriodicalId":115821,"journal":{"name":"Proceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125087883","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}
Background: At present, with the continuous development of the epidemic, scholars at home and abroad have paid great attention to Corona Virus Discrease 2019(COVID-19), especially with SARS, comprehensive biology combined and carried out a series of research and discussion. Research on such topics has gradually increased, but the hot topics of research still not clear. Purpose: The goal of the research was to perform a systematic review on the use of biomedicine in medical research with the aim of understanding the global progress on COVID-19 research outcomes, content, methods, and study groups involved. Methods: The SARS documents were retrieved in the core collection of Web of Science, and analyze the data with Excel and VOSviewer.to perform bibliometric analysis of publication trends, author orders, countries, institutions, collaboration relationships, research hot spots, diseases studied, and research methods. Data Synthesis: A total of 50,744 original research articles were included. The number of articles published in the first 3 years showed an overall upward trend and then gradually declined to basically the same. It accounts for 2.087% of the total number of posts, reaching a peak of 31050 in 2020, accounting for 61.190% of the total. From 6,976 journals, 18 journals with more than 250 articles published, more than half of the journals are in JCR Zone 1, and most of the influence factors are between 2-7 points; the included literature involves 61684 authors, and most of the authors are scattered. The included literature involved 9738 institutions. Among the first 18 institutions, more than 80% were colleges and hospitals. A total of 37,817 keywords are listed, with an average of 2.65 keywords. The top 7 in frequency of use are acute lung injury, acute respiratory distress syndrome, respiratory-distress-syndrome, aras, mechanical ventilation, and mortality. The centrality is more than 7000, and the number of occurrences is more than 1000. The research direction of SARS focuses on: RESPIRATORY SYSTEM, INFECTIOUS DISEASES, PUBLIC ENVIRONMENTAL OCCUPATIONAL HEALTH, BIOCHEMISTRY MOLECULAR BIOLOGY and HEALTH CARE SCIENCES SERVICES, which are all higher than 27% of the total, and the records are higher than 13,900. High-frequency keyword clustering and overlay view analysis: (1) In terms of mechanism research, gene structure, virus detection, outbreak prevention and protection, etc. are the major research hotspots. (2) In terms of target therapy, animal molecular experiments such as protein, activation, modle mode, potent inhibitors, laboratory tests, and immune response are the research hotspots. Conclusions: Based on the research of SARS literature, preliminary research suggests that COVID-19 research will increase rapidly by multiples, and the popularity of research continues. In terms of public health and epidemic prevention, the world still needs to form a stronger team. It needs to do a good job of inheritance, achieve full prevention an
背景:当前，随着疫情的不断发展，国内外学者对2019冠状病毒病(COVID-19)给予了高度关注，特别是与SARS、综合生物学相结合，开展了一系列研究和探讨。这类课题的研究逐渐增多，但研究热点仍不明确。目的:本研究的目的是对生物医学在医学研究中的应用进行系统综述，目的是了解COVID-19研究成果、内容、方法和涉及的研究组的全球进展。方法:从Web of Science核心馆藏中检索SARS文献，应用Excel和VOSviewer进行数据分析。对出版趋势、作者顺序、国家、机构、合作关系、研究热点、研究疾病和研究方法进行文献计量分析。数据综合:共纳入50,744篇原创研究论文。前3年发表的文章数量总体呈上升趋势，之后逐渐下降，基本持平。占总岗位数的2.087%，到2020年达到峰值31050个，占总岗位数的61.190%。6976种期刊中，发表文章超过250篇的期刊有18种，超过一半的期刊处于JCR 1区，影响因子大部分在2-7分之间;纳入文献涉及作者61684人，作者多为散居。纳入文献涉及9738家机构。在前18个机构中，80%以上是高等院校和医院。共列出37817个关键词，平均2.65个关键词。使用频率排名前7位的分别是急性肺损伤、急性呼吸窘迫综合征、呼吸窘迫综合征、急性呼吸窘迫综合征、机械通气和死亡率。中心性大于7000次，出现次数大于1000次。SARS的研究方向主要集中在:呼吸系统、传染病、公共环境职业卫生、生物化学分子生物学和卫生保健科学服务，均高于总数的27%，记录量超过1.39万份。高频关键词聚类与叠加视图分析:(1)在机制研究方面，基因结构、病毒检测、疫情防控等是主要研究热点。(2)在靶向治疗方面，蛋白质、活化、模型模式、强效抑制剂、实验室试验、免疫应答等动物分子实验是研究热点。结论:基于对SARS文献的研究，初步研究表明，COVID-19研究将以数倍的速度快速增加，研究的普及程度将继续提高。在公共卫生和疫情防控方面，世界各国仍然需要形成一个更强大的团队。需要做好传承，做到充分防控绩效，创新成果，借鉴常用词聚类和知识图挖掘潜力领域，促进权威创新的出现。研究趋势将从诊断和治疗的发展扩展到人-社会-生物环境，为全球抗击疫情作出重要贡献。
{"title":"Visual analysis based on the research of SARS and COVID-19: a 20-year bibliometric study","authors":"Qifang Liang, Buping Liu, Chunping Liu, Wenxing Liu, Xiaoxue Han, Limei Wan, Xiaobo Chen, Peng wu, Hongyu Li, Yujiao Sun, Yubin Yang, Weixiong Chen","doi":"10.1145/3448748.3448756","DOIUrl":"https://doi.org/10.1145/3448748.3448756","url":null,"abstract":"Background: At present, with the continuous development of the epidemic, scholars at home and abroad have paid great attention to Corona Virus Discrease 2019(COVID-19), especially with SARS, comprehensive biology combined and carried out a series of research and discussion. Research on such topics has gradually increased, but the hot topics of research still not clear. Purpose: The goal of the research was to perform a systematic review on the use of biomedicine in medical research with the aim of understanding the global progress on COVID-19 research outcomes, content, methods, and study groups involved. Methods: The SARS documents were retrieved in the core collection of Web of Science, and analyze the data with Excel and VOSviewer.to perform bibliometric analysis of publication trends, author orders, countries, institutions, collaboration relationships, research hot spots, diseases studied, and research methods. Data Synthesis: A total of 50,744 original research articles were included. The number of articles published in the first 3 years showed an overall upward trend and then gradually declined to basically the same. It accounts for 2.087% of the total number of posts, reaching a peak of 31050 in 2020, accounting for 61.190% of the total. From 6,976 journals, 18 journals with more than 250 articles published, more than half of the journals are in JCR Zone 1, and most of the influence factors are between 2-7 points; the included literature involves 61684 authors, and most of the authors are scattered. The included literature involved 9738 institutions. Among the first 18 institutions, more than 80% were colleges and hospitals. A total of 37,817 keywords are listed, with an average of 2.65 keywords. The top 7 in frequency of use are acute lung injury, acute respiratory distress syndrome, respiratory-distress-syndrome, aras, mechanical ventilation, and mortality. The centrality is more than 7000, and the number of occurrences is more than 1000. The research direction of SARS focuses on: RESPIRATORY SYSTEM, INFECTIOUS DISEASES, PUBLIC ENVIRONMENTAL OCCUPATIONAL HEALTH, BIOCHEMISTRY MOLECULAR BIOLOGY and HEALTH CARE SCIENCES SERVICES, which are all higher than 27% of the total, and the records are higher than 13,900. High-frequency keyword clustering and overlay view analysis: (1) In terms of mechanism research, gene structure, virus detection, outbreak prevention and protection, etc. are the major research hotspots. (2) In terms of target therapy, animal molecular experiments such as protein, activation, modle mode, potent inhibitors, laboratory tests, and immune response are the research hotspots. Conclusions: Based on the research of SARS literature, preliminary research suggests that COVID-19 research will increase rapidly by multiples, and the popularity of research continues. In terms of public health and epidemic prevention, the world still needs to form a stronger team. It needs to do a good job of inheritance, achieve full prevention an","PeriodicalId":115821,"journal":{"name":"Proceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129847483","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}
Chinese ancient poetry has been a favorite literary form and is still very popular after thousands of years. As opposed to free language, poetry has the characteristics of aestheticism and conciseness. People can easily judge the quality of ancient poetry, so the generation of ancient poetry can be used as an important method for beginners to learn NLG models and judge the performance of the models. Therefore, we present a novel system called LiBai to facilitate the comprehensive generation and analysis of poetry. The system LiBai includes two main functionality modules - a versatile model library and a user-friendly and interactive studio. LiBai can help user: 1) learn popular poetry generation models systematically, including the model introduction, network structures and performances; 2) through adjusting various model parameters, interactive training, predicting and directly apply these models on real data easily and 3) simply and quickly analyse the generated poetry.
{"title":"A Learning and Practicing System to Support Effective Poetry Generation Based on Neural Network","authors":"Tianqi Gao","doi":"10.1145/3448748.3448751","DOIUrl":"https://doi.org/10.1145/3448748.3448751","url":null,"abstract":"Chinese ancient poetry has been a favorite literary form and is still very popular after thousands of years. As opposed to free language, poetry has the characteristics of aestheticism and conciseness. People can easily judge the quality of ancient poetry, so the generation of ancient poetry can be used as an important method for beginners to learn NLG models and judge the performance of the models. Therefore, we present a novel system called LiBai to facilitate the comprehensive generation and analysis of poetry. The system LiBai includes two main functionality modules - a versatile model library and a user-friendly and interactive studio. LiBai can help user: 1) learn popular poetry generation models systematically, including the model introduction, network structures and performances; 2) through adjusting various model parameters, interactive training, predicting and directly apply these models on real data easily and 3) simply and quickly analyse the generated poetry.","PeriodicalId":115821,"journal":{"name":"Proceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133250659","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}
Intracranial Hemorrhage (ICH), a dangerous and devastating medical emergency, affects thousands of patients every year around the world. In the clinical settings, Computer Tomography (CT), is widely used for diagnosis of neurological diseases. In the situation of Intracranial Hemorrhage, not only saving time is critically important, but also the expertise to accurately diagnose and locate ICH is imperative. However, there are not always enough doctors working in the emergency expert in the field of ICH, and the results from using only deep learning models are not always reliable. Three neural networks, VGG-19, Resnet-101, and DenseNet-201 were trained separately on preprocessed the Intracranial hemorrhage data with labels and used the Grad-CAM method to produce a saliency map by visualizing the process of the network making a decision regarding to specific class index, thus increasing the interpretability of the results. We tested the networks' performances on our preprocessed CT data, and their differences produced saliency maps. Three experiments were designed and conducted to help us understand our models' performance and predictions in different contexts. First, we observed the differences between the pre-trained deep learning model and the unpre-trained deep learning models. Second, we observed how the performance and Grad-CAM results would differ when the images were normalized at different Window values. Third, we merged the six Grad-CAM images generated by the six class indices for each image into a single image and fed it into the network to observe the results. To further demonstrate the potential application of our deep learning models, we used trained models to make a GUI software called ICH Deep Learning Detector in python with the PyQt5 library to simplify the process of doctors using the deep learning model and learning from predictions.
{"title":"An Interpretable Deep Learning System for Automatic Intracranial Hemorrhage Diagnosis with CT Image","authors":"Zhongxuan Wang, Leiming Wu, Xiangcheng Ji","doi":"10.1145/3448748.3448803","DOIUrl":"https://doi.org/10.1145/3448748.3448803","url":null,"abstract":"Intracranial Hemorrhage (ICH), a dangerous and devastating medical emergency, affects thousands of patients every year around the world. In the clinical settings, Computer Tomography (CT), is widely used for diagnosis of neurological diseases. In the situation of Intracranial Hemorrhage, not only saving time is critically important, but also the expertise to accurately diagnose and locate ICH is imperative. However, there are not always enough doctors working in the emergency expert in the field of ICH, and the results from using only deep learning models are not always reliable. Three neural networks, VGG-19, Resnet-101, and DenseNet-201 were trained separately on preprocessed the Intracranial hemorrhage data with labels and used the Grad-CAM method to produce a saliency map by visualizing the process of the network making a decision regarding to specific class index, thus increasing the interpretability of the results. We tested the networks' performances on our preprocessed CT data, and their differences produced saliency maps. Three experiments were designed and conducted to help us understand our models' performance and predictions in different contexts. First, we observed the differences between the pre-trained deep learning model and the unpre-trained deep learning models. Second, we observed how the performance and Grad-CAM results would differ when the images were normalized at different Window values. Third, we merged the six Grad-CAM images generated by the six class indices for each image into a single image and fed it into the network to observe the results. To further demonstrate the potential application of our deep learning models, we used trained models to make a GUI software called ICH Deep Learning Detector in python with the PyQt5 library to simplify the process of doctors using the deep learning model and learning from predictions.","PeriodicalId":115821,"journal":{"name":"Proceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130953941","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}
Object detection is a core part of an intelligent surveillance system and a fundamental algorithm in the field of identity identification, which is of great practical importance. Since the YOLO series algorithms have good results in terms of accuracy and speed, YOLO and each subsequent version have been surpassing. Thus, in this paper, it carries out experiments on three versions of popular YOLO models such as yolov3, yolov4, and yolov5 (yolov5l, yolov5m, yolov5s, yolov5x). The performance of the three versions of YOLO model is analyzed and summarized by training and predicting the public VOC dataset. Results showed that the yolov4 model is higher than the yolov3 model in terms of mAP values, but slightly lower in terms of speed, while the yolov5 series model is better than the yolov3 and yolov4 models both in terms of mAP values and speed.
{"title":"Performance Validation of Yolo Variants for Object Detection","authors":"Kaiyue Liu, Haitong Tang, Shuang He, Qin Yu, Yulong Xiong, Ni-zhuan Wang","doi":"10.1145/3448748.3448786","DOIUrl":"https://doi.org/10.1145/3448748.3448786","url":null,"abstract":"Object detection is a core part of an intelligent surveillance system and a fundamental algorithm in the field of identity identification, which is of great practical importance. Since the YOLO series algorithms have good results in terms of accuracy and speed, YOLO and each subsequent version have been surpassing. Thus, in this paper, it carries out experiments on three versions of popular YOLO models such as yolov3, yolov4, and yolov5 (yolov5l, yolov5m, yolov5s, yolov5x). The performance of the three versions of YOLO model is analyzed and summarized by training and predicting the public VOC dataset. Results showed that the yolov4 model is higher than the yolov3 model in terms of mAP values, but slightly lower in terms of speed, while the yolov5 series model is better than the yolov3 and yolov4 models both in terms of mAP values and speed.","PeriodicalId":115821,"journal":{"name":"Proceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124085508","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}
Ibuprofen is an antipyretic and analgesic anti-inflammatory drug. In order to study the pharmacokinetic characteristics of Chinese healthy adults after ibuprofen injection, this article establishes a pharmacokinetic nonlinear mixed effect model to analyze the blood concentration and clinical characteristics of ibuprofen of 12 healthy volunteers after a single dose. Three statistical methods, FO (First-order), FOCE-I (First-order conditional estimation with interaction), and BAYES (Markov chain Monte Carlo Bayesian) are used to estimate the parameters of the population pharmacokinetics, then analyze and compare in terms of relative standard error, goodness of fit and convergence speed. BAYES is suitable for higher estimation requirements of goodness of fit, FOCE-I is suitable for estimation that needs to consider residuals and inter-individual variation, and FO is suitable for the evaluation of massive medical data, in which the estimands needs to be obtained with higher convergence speed.
{"title":"Comparison of Pharmacokinetic Effects of Ibuprofen Based on Three Statistical Methods","authors":"Wanqing Peng, Haoxuan Li","doi":"10.1145/3448748.3448768","DOIUrl":"https://doi.org/10.1145/3448748.3448768","url":null,"abstract":"Ibuprofen is an antipyretic and analgesic anti-inflammatory drug. In order to study the pharmacokinetic characteristics of Chinese healthy adults after ibuprofen injection, this article establishes a pharmacokinetic nonlinear mixed effect model to analyze the blood concentration and clinical characteristics of ibuprofen of 12 healthy volunteers after a single dose. Three statistical methods, FO (First-order), FOCE-I (First-order conditional estimation with interaction), and BAYES (Markov chain Monte Carlo Bayesian) are used to estimate the parameters of the population pharmacokinetics, then analyze and compare in terms of relative standard error, goodness of fit and convergence speed. BAYES is suitable for higher estimation requirements of goodness of fit, FOCE-I is suitable for estimation that needs to consider residuals and inter-individual variation, and FO is suitable for the evaluation of massive medical data, in which the estimands needs to be obtained with higher convergence speed.","PeriodicalId":115821,"journal":{"name":"Proceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124087350","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}
Alzheimer's disease (AD) is a kind of brain disease, which causes abnormal memory loss, thought chaos, and behavior confusion. There are still no effective methods or medicine to prevent the worsening of AD. The best way at present is to reduce the risk of getting AD. In this paper, the author constructs an artificial neural network (ANN) and XGBoost to determine whether or not a person gets AD, by analyzing how related factors impact the group a person belonging to. The Open Access Series of Imaging Studies (OASIS) longitudinal MRI data were analyzed cross age, gender, education, social economic status (SES), mini mental state examination (MMSE), estimated total intracranial volume (eTIV), clinical dementia rating (CDR), normalized whole brain volume (nBWV), and atlas scaling factor (ASF). The purpose of this study is to decide whether a person is demented or not by comparing two classic methods, thus to explore the advantages and disadvantages of two models in real world application. The analysis is helpful to predict and model the different features in non-demented and demented people, therefore giving a clearer perspective for reducing people's risk of dementia by making appropriate adjustments. The accuracy of testing with ANN is 89.3%, with 37 matched non-demented and 30 matched demented out of 75 observations, which is 20% testing set. The accuracy of fitting the data is 93.3% for XGBoost, with 38 matched non-demented and 32 matched demented out of 75 observations. K-fold cross validation is applied to improve the accuracy rate. The accuracy is then improved to 95.6% for ANN and 99.6% for XGBoost. In conclusion, the result is consistent with the former literature study, showing that the machine learning method is more accurate than deep learning.
{"title":"Application and Comparison of Artificial Neural Networks and XGBoost on Alzheimer's Disease","authors":"Xinyu Sun","doi":"10.1145/3448748.3448765","DOIUrl":"https://doi.org/10.1145/3448748.3448765","url":null,"abstract":"Alzheimer's disease (AD) is a kind of brain disease, which causes abnormal memory loss, thought chaos, and behavior confusion. There are still no effective methods or medicine to prevent the worsening of AD. The best way at present is to reduce the risk of getting AD. In this paper, the author constructs an artificial neural network (ANN) and XGBoost to determine whether or not a person gets AD, by analyzing how related factors impact the group a person belonging to. The Open Access Series of Imaging Studies (OASIS) longitudinal MRI data were analyzed cross age, gender, education, social economic status (SES), mini mental state examination (MMSE), estimated total intracranial volume (eTIV), clinical dementia rating (CDR), normalized whole brain volume (nBWV), and atlas scaling factor (ASF). The purpose of this study is to decide whether a person is demented or not by comparing two classic methods, thus to explore the advantages and disadvantages of two models in real world application. The analysis is helpful to predict and model the different features in non-demented and demented people, therefore giving a clearer perspective for reducing people's risk of dementia by making appropriate adjustments. The accuracy of testing with ANN is 89.3%, with 37 matched non-demented and 30 matched demented out of 75 observations, which is 20% testing set. The accuracy of fitting the data is 93.3% for XGBoost, with 38 matched non-demented and 32 matched demented out of 75 observations. K-fold cross validation is applied to improve the accuracy rate. The accuracy is then improved to 95.6% for ANN and 99.6% for XGBoost. In conclusion, the result is consistent with the former literature study, showing that the machine learning method is more accurate than deep learning.","PeriodicalId":115821,"journal":{"name":"Proceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125606058","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}
The use of various algorithm models based on computer platform is a universal and supporting core technology. When the development team or decision maker faces some important and difficult problems, the computer algorithm model can be used to calculate, optimize and solve complex problems, and complete the simulation by model simulation steps. In this paper, through the combination of genetic algorithm and particle swarm optimization, firstly, the optimization method of e-commerce website structure was put forward. Then, the real data was used to check the computation. Finally, the simulation analysis was carried out by simulation software. The simulation results show that it is feasible and reliable to optimize the structure of e-commerce website by using genetic algorithm and particle swarm optimization.
{"title":"Optimization of E-commerce Website Structure based on Particle Swarm Optimization and Genetic Algorithm","authors":"J. Hu","doi":"10.1145/3448748.3448777","DOIUrl":"https://doi.org/10.1145/3448748.3448777","url":null,"abstract":"The use of various algorithm models based on computer platform is a universal and supporting core technology. When the development team or decision maker faces some important and difficult problems, the computer algorithm model can be used to calculate, optimize and solve complex problems, and complete the simulation by model simulation steps. In this paper, through the combination of genetic algorithm and particle swarm optimization, firstly, the optimization method of e-commerce website structure was put forward. Then, the real data was used to check the computation. Finally, the simulation analysis was carried out by simulation software. The simulation results show that it is feasible and reliable to optimize the structure of e-commerce website by using genetic algorithm and particle swarm optimization.","PeriodicalId":115821,"journal":{"name":"Proceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126095612","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}
Objective: This paper is to explore the effect of Tongyu Decoction on neurological deficits and rehabilitation effects in patients with cerebral hemorrhage during recovery. Methods: A total of 100 patients with cerebral hemorrhage in recovery period were selected as the research objects in this study. The study was carried out from December 2019 to December 2020. The random number table method was applied, and 100 patients were divided into equal groups, with 50 patients in each group named experimental group and control group, which applied conventional Western medicine and Tongyu Decoction respectively, and then compared the treatment of the two groups. Results: Before treatment, there was no significant difference in the NIHSS scores of the two groups of patients, P>0.05. After treatment, the scores all declined, but the decline in the experimental group was more significant, which was quite different from the control group, P<0.05, at the same time the rehabilitation efficiency and satisfaction of the experimental group were 94.00% and 96.00%, which were significantly higher than those of the control group. The data differences between the groups were relatively significant, P<0.05, the experimental group had better results. Conclusion: The application of Tongyu Decoction in patients with cerebral hemorrhage during the recovery period can effectively promote the recovery of patients with neurological deficits, strengthen the rehabilitation effect, and improve patient satisfaction. The clinical intervention effect is significant.
{"title":"Curative Effect of Tongyu Decoction on Neurological Deficit and Rehabilitation Effect of Patients with Cerebral Hemorrhage in Recovery Period","authors":"Sheng-fang Zhou","doi":"10.1145/3448748.3448772","DOIUrl":"https://doi.org/10.1145/3448748.3448772","url":null,"abstract":"Objective: This paper is to explore the effect of Tongyu Decoction on neurological deficits and rehabilitation effects in patients with cerebral hemorrhage during recovery. Methods: A total of 100 patients with cerebral hemorrhage in recovery period were selected as the research objects in this study. The study was carried out from December 2019 to December 2020. The random number table method was applied, and 100 patients were divided into equal groups, with 50 patients in each group named experimental group and control group, which applied conventional Western medicine and Tongyu Decoction respectively, and then compared the treatment of the two groups. Results: Before treatment, there was no significant difference in the NIHSS scores of the two groups of patients, P>0.05. After treatment, the scores all declined, but the decline in the experimental group was more significant, which was quite different from the control group, P<0.05, at the same time the rehabilitation efficiency and satisfaction of the experimental group were 94.00% and 96.00%, which were significantly higher than those of the control group. The data differences between the groups were relatively significant, P<0.05, the experimental group had better results. Conclusion: The application of Tongyu Decoction in patients with cerebral hemorrhage during the recovery period can effectively promote the recovery of patients with neurological deficits, strengthen the rehabilitation effect, and improve patient satisfaction. The clinical intervention effect is significant.","PeriodicalId":115821,"journal":{"name":"Proceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115176010","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}
The development of artificial intelligence promotes the great progress of medical imaging diagnosis. Based on this, this paper reviews and discusses the medical image diagnosis based on artificial intelligence in recent years, introduces the procedure of medical image diagnosis, the algorithms involved and the key progress, analyzes the shortcomings of the current technology, and the possible development direction in the future.
{"title":"Application of Artificial Intelligence in Medical Imaging Diagnosis","authors":"Baoming Zhang, Weili Yue, Qian Liu, Shimin Hu","doi":"10.1145/3448748.3448810","DOIUrl":"https://doi.org/10.1145/3448748.3448810","url":null,"abstract":"The development of artificial intelligence promotes the great progress of medical imaging diagnosis. Based on this, this paper reviews and discusses the medical image diagnosis based on artificial intelligence in recent years, introduces the procedure of medical image diagnosis, the algorithms involved and the key progress, analyzes the shortcomings of the current technology, and the possible development direction in the future.","PeriodicalId":115821,"journal":{"name":"Proceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126929779","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}
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