2022 2nd International Conference on Bioinformatics and Intelligent Computing最新文献_第5页

A Model Based on Radiomics and Machine Learning in Glioma Grading 基于放射组学和机器学习的神经胶质瘤分级模型

2022 2nd International Conference on Bioinformatics and Intelligent Computing Pub Date : 2022-01-21 DOI: 10.1145/3523286.3524605

Junxi Wang, Jianchao Zeng, Xiaoqing Yu, Jingang Liu

引用次数: 0

Research on the Design of Medical Big Data Task Scheduling Based on SOS Algorithm: Preparation of Camera-Ready Contributions to SCITEPRESS Proceedings 基于SOS算法的医疗大数据任务调度设计研究——为sciitepress论文集准备可拍照稿件

2022 2nd International Conference on Bioinformatics and Intelligent Computing Pub Date : 2022-01-21 DOI: 10.1145/3523286.3524566

Yan Zeng, Jianrong Li

引用次数: 0

Regularity of Heart Rate Fluctuations Analysis in Congestive Heart Failure Patients Using Information-Based Similarity 基于信息相似性的充血性心力衰竭患者心率波动规律分析

2022 2nd International Conference on Bioinformatics and Intelligent Computing Pub Date : 2022-01-21 DOI: 10.1145/3523286.3524528

Yinghao Guo, Fangze Peng

{"title":"Regularity of Heart Rate Fluctuations Analysis in Congestive Heart Failure Patients Using Information-Based Similarity","authors":"Yinghao Guo, Fangze Peng","doi":"10.1145/3523286.3524528","DOIUrl":"https://doi.org/10.1145/3523286.3524528","url":null,"abstract":"Congestive Heart Failure (CHF) is a chronic progressive condition that affects the pumping power of your heart muscle. There are a number of works investigating Obstructive Sleep Apnea (OSA) detection based on heart rate variability and obtain outstanding results. Therefore, using HRV analysis for the screening of CHF patients has great potential. This study included 30 electrocardiogram (ECG) recordings (15 CHF recordings and 15 normal recordings) from the PhysioNet database. These recordings included 24h RR interval data and were divided into 5-minnute segments. Comparing with traditional time-domain analysis and frequency-domain analysis, information-based similarity (IBS) has a better performance on showing significant differences between normal group and CHF group (p < 0.001). The accuracies of time-domain analysis and frequency-domain analysis in the CHF detection are 86.7% and 83.3%, respectively, while IBS performed an accuracy of 86.7% with a better balance between sensitivity and specificity. This research find that the similarity of heart rate decreased in CHF group because of the low-level similarity of adjacent RR segments. This finding is probably the reason that CHF patients have arrhythmia. Consequently, this IBS method has certain clinical significance, and could be used to detect CHF.","PeriodicalId":268165,"journal":{"name":"2022 2nd International Conference on Bioinformatics and Intelligent Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130453267","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

Predicting Center of Pressure Velocity Based on Regional Plantar Force in Elderly Men Using Artificial Neural Networks 基于区域足底力的老年男性压力速度中心的人工神经网络预测

2022 2nd International Conference on Bioinformatics and Intelligent Computing Pub Date : 2022-01-21 DOI: 10.1145/3523286.3524688

Xuanzhen Cen, István Bíró

引用次数: 0

Optimization of Big Data Mining Algorithm Based on Spark Framework: Preparation of Camera-Ready Contributions to SCITEPRESS Proceedings 基于Spark框架的大数据挖掘算法优化:为sciitepress论文集准备相机稿件

2022 2nd International Conference on Bioinformatics and Intelligent Computing Pub Date : 2022-01-21 DOI: 10.1145/3523286.3524685

Yan Zeng, Jun Yu Li

引用次数: 0

Application Scenario based Design of the Health Code Platform 基于应用场景的健康码平台设计

2022 2nd International Conference on Bioinformatics and Intelligent Computing Pub Date : 2022-01-21 DOI: 10.1145/3523286.3524516

J. Shan, Zhao Wang, Yawen Pan

引用次数: 0

Research on the Network Evolution of Rainstorm Disaster Chains 暴雨灾害链网络演化研究

2022 2nd International Conference on Bioinformatics and Intelligent Computing Pub Date : 2022-01-21 DOI: 10.1145/3523286.3524538

Shuangyan Wu, Yingbo Ji, Yuan Qi

引用次数: 1

A Hypertension Risk Prediction Model Based on Improve Random Forest 基于改进随机森林的高血压风险预测模型

2022 2nd International Conference on Bioinformatics and Intelligent Computing Pub Date : 2022-01-21 DOI: 10.1145/3523286.3524582

Hongyang Wu, Xiaoyu Song, Linze Zhu, Xiaobei Feng, Yifan Li, Jiahao Chang

{"title":"A Hypertension Risk Prediction Model Based on Improve Random Forest","authors":"Hongyang Wu, Xiaoyu Song, Linze Zhu, Xiaobei Feng, Yifan Li, Jiahao Chang","doi":"10.1145/3523286.3524582","DOIUrl":"https://doi.org/10.1145/3523286.3524582","url":null,"abstract":"In order to reduce the serious consequences of chronic diseases, this paper proposes a hypertension risk prediction model based on improved random forest, which provides an effective technical means for early warning of hypertension. The original data set with unbalanced samples is processed by the synthetic minority oversampling technique (SMOTE) to form a balanced data set. Then improve the random forest algorithm based on similarity optimization and deep optimization, and finally establish a prediction model. It is compared with the four machine learning algorithms of linear regression (LR), artificial neural network (ANN), support vector machine (SVM) and CatBoost. ROC curve and AUC are used as the evaluation indicators of the model. The experimental results show that the prediction accuracy of the model based on the improved random forest algorithm is higher, with an AUC value of 0.8697, which is better than the other four algorithms. The improved random forest algorithm has certain feasibility in hypertension risk prediction. This method has a better effect in predicting the risk of hypertension, which is better than other traditional methods, can provide more accurate judgments, and provide better results for early warning and prevention of hypertension.","PeriodicalId":268165,"journal":{"name":"2022 2nd International Conference on Bioinformatics and Intelligent Computing","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115634953","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

Research on Ultrasonic Image Segmentation of Thyroid Nodules Based on Improved U-net++ 基于改进U-net++的甲状腺结节超声图像分割研究

2022 2nd International Conference on Bioinformatics and Intelligent Computing Pub Date : 2022-01-21 DOI: 10.1145/3523286.3524603

Chaoyi Chen, Bo Xu, Ying Wu, Kaiwen Wu, Cuier Tan

{"title":"Research on Ultrasonic Image Segmentation of Thyroid Nodules Based on Improved U-net++","authors":"Chaoyi Chen, Bo Xu, Ying Wu, Kaiwen Wu, Cuier Tan","doi":"10.1145/3523286.3524603","DOIUrl":"https://doi.org/10.1145/3523286.3524603","url":null,"abstract":"The ultrasound image of thyroid nodules has low contrast and severe speckle noise, and the morphology of thyroid nodules in different patients is quite different, which makes it extremely difficult for doctors to accurately and quickly diagnose the nodules. In order to accurately segment the thyroid nodules from the ultrasound image, the paper improves the U-Net++ network. Based on the U-Net++ model, EfficientDet is used as the encoder, and the CSSE block is merged in the encoder and decoder to improve performance. In addition, the paper improves the network structure and reduces the number of model parameters. After testing 720 ultrasound images of thyroid nodules, the improved U-Net++ image segmentation has an average Dice coefficient of 0.9213, an average accuracy of 0.9262, an average recall rate of 0.9011, and an average F1 score of 0.9202. The Dice coefficient of the improved algorithm segmentation is 9.01% higher than that of U-Net++. The improved algorithm is of great significance for the application of automatic segmentation of ultrasound images of thyroid nodules in actual clinical medicine.","PeriodicalId":268165,"journal":{"name":"2022 2nd International Conference on Bioinformatics and Intelligent Computing","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123392389","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

An Illustration of the False Memory Process Based on The Artificial Neural Networks Model 基于人工神经网络模型的错误记忆过程描述

2022 2nd International Conference on Bioinformatics and Intelligent Computing Pub Date : 2022-01-21 DOI: 10.1145/3523286.3524591

Xin Du

引用次数: 1