Proceedings of the 1st International Symposium on Artificial Intelligence in Medical Sciences最新文献_第3页

Mathematic Models of Interspecific Relationships 种间关系的数学模型

Proceedings of the 1st International Symposium on Artificial Intelligence in Medical Sciences Pub Date : 2020-09-11 DOI: 10.1145/3429889.3429892

Wan-Yu Zhang

引用次数: 0

Predictive Modeling of Diabetic Kidney Disease using Random Forest Algorithm along with Features Selection 基于随机森林算法和特征选择的糖尿病肾病预测建模

Proceedings of the 1st International Symposium on Artificial Intelligence in Medical Sciences Pub Date : 2020-09-11 DOI: 10.1145/3429889.3429894

Hongxia Xu, Yonghui Kong, Shaofeng Tan

{"title":"Predictive Modeling of Diabetic Kidney Disease using Random Forest Algorithm along with Features Selection","authors":"Hongxia Xu, Yonghui Kong, Shaofeng Tan","doi":"10.1145/3429889.3429894","DOIUrl":"https://doi.org/10.1145/3429889.3429894","url":null,"abstract":"At present, the number of diabetes mellitus patients in China ranks first in the world, and diabetic kidney disease is the most common disease in complications. Therefore, it is necessary to establish a predictive model for early diagnosis of diabetic kidney disease. The model predicts the risk of diabetic kidney disease in the general Asian population, and recognizes high-risk groups, then warns the onset of diabetes. The data were obtained from the electronic medical record of patients in Beijing Pinggu Hospital. Twenty-nine initial candidate indicators including age, ALB, and A/C were selected. The random forest algorithm was used to predict diabetic kidney disease, and the classification accuracy was 89.831%. The importance weight ratio of each factor index was also given, Microalbuminuria (ALB), albumin-to-creatinine ratio (A/C), serum creatinine (SCr), Serum albumin (umALB), and blood urea nitrogen (BUN) accounted for a relatively high proportion of the weight of the characteristic variables. So these five indicators can be the primary indicators of our classification prediction, and the accuracy can reach 87.453%. Some other typical classification algorithms, liking KNN, logistic regression, and decision tree, were compared to classify and predict diabetic kidney disease, and precision recall f1-score and area AUC under ROC curve were used to evaluate these models. By experiments, random forest model was better than other algorithm models on both the classification accuracy and the evaluation indicators. The results can be applied to the screening of patients with high risk of diabetic kidney disease and the guidance of risk intervention measures. Consequently, the detection rate of undiagnosed diabetic kidney disease in the population can be improved, and the prevention effect of diabetic kidney disease can be enhanced as well.","PeriodicalId":315899,"journal":{"name":"Proceedings of the 1st International Symposium on Artificial Intelligence in Medical Sciences","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123245077","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: 2

A Comparison between Generic Algorithm and Particle Swarm Optimization 通用算法与粒子群算法的比较

Proceedings of the 1st International Symposium on Artificial Intelligence in Medical Sciences Pub Date : 2020-09-11 DOI: 10.1145/3429889.3430294

Yonghai Yu, ShuE Yin

引用次数: 0

Research on predictive model of liver cirrhosis complicated with liver cancer based on support vector machine 基于支持向量机的肝硬化合并肝癌预测模型研究

Proceedings of the 1st International Symposium on Artificial Intelligence in Medical Sciences Pub Date : 2020-09-11 DOI: 10.1145/3429889.3430087

Huifang Lai, Zhihui Huang, Chi Chen, Feifei Chen, Hui Wang, Xiaomin Zhu

{"title":"Research on predictive model of liver cirrhosis complicated with liver cancer based on support vector machine","authors":"Huifang Lai, Zhihui Huang, Chi Chen, Feifei Chen, Hui Wang, Xiaomin Zhu","doi":"10.1145/3429889.3430087","DOIUrl":"https://doi.org/10.1145/3429889.3430087","url":null,"abstract":"Purpose: This article aims to build a predictive model of liver cirrhosis complicated with liver cancer, hoping to provide an important reference for the clinical diagnosis of liver disease. Method: First, through single factor analysis, pre-process the biochemical index data of 265 patients with liver cirrhosis collected from a special hospital in Fuzhou City, and obtain the biochemical index fields with a high degree of relevance to liver cirrhosis and liver cancer. On the basis of the set, the multi-factor Logistic regression analysis is used to construct the final prediction model. Compare this model with the prediction model constructed by support vector machine and observe the final result. Results: The accuracy of the prediction model of cirrhosis complicated with liver cancer using Logistic regression analysis method was 80.75%, and the sensitivity was 77.55%. The accuracy and sensitivity of the prediction model of cirrhosis complicated with liver cancer using the support vector machine method reached 81.48%. Support Vector Machine has better promotion ability in this prediction model. Conclusion: The support vector machine method can better predict liver cirrhosis complicated with liver cancer. Its accuracy is higher than that of Logistic regression analysis. It has higher efficiency value and can provide a scientific reference for the prediction of liver cirrhosis complicated with liver cancer.","PeriodicalId":315899,"journal":{"name":"Proceedings of the 1st International Symposium on Artificial Intelligence in Medical Sciences","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128595454","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

Fabric-based ECG Electorde Jet Printing Formation and Performance Test 基于织物的ECG电极喷射打印成形及性能测试

Proceedings of the 1st International Symposium on Artificial Intelligence in Medical Sciences Pub Date : 2020-09-11 DOI: 10.1145/3429889.3429922

Yuan Xiao, Weibo Yun, Chengkun Zhang

{"title":"Fabric-based ECG Electorde Jet Printing Formation and Performance Test","authors":"Yuan Xiao, Weibo Yun, Chengkun Zhang","doi":"10.1145/3429889.3429922","DOIUrl":"https://doi.org/10.1145/3429889.3429922","url":null,"abstract":"Given the complicated process and high cost in the fabric electrode preparation process, this paper uses a combination of droplet jet technology and chemical deposition technology to directly print the conductive layer on the surface of the fabric to realize the preparation of the fabric electrode. The microscopic morphology and conductivity of the conductive layer before packaging are studied, and the AC impedance test of the fabric electrode after packaging and the acquisition of human ECG signals are carried out. The results show that the average diameter of the silver particles in the conductive layer of the fabric electrode is about 15 &mgr; m, the thickness is about 100 &mgr; m, and the conductivity of the conductive layer is 8.658 × 105 S/m. The trend of the AC impedance of the fabric electrode is similar to that of the standard Ag-AgCl electrode, and the impedance of the plain weave fabric electrode is lower than the standard gel Ag-AgCl electrode at 10~45 Hz; the collected ECG signal is similar to the R wave amplitude measured by the standard Ag-AgCl electrode, and the ECG signal has a clear spectral composition, which verifies the feasibility of the fabric electrode to collect ECG signal.","PeriodicalId":315899,"journal":{"name":"Proceedings of the 1st International Symposium on Artificial Intelligence in Medical Sciences","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116109892","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

Spectrum Analysis of Bone-conducted Speech: A Study Based on Intelligibility Scoring 骨传导语音频谱分析:基于可理解度评分的研究

Proceedings of the 1st International Symposium on Artificial Intelligence in Medical Sciences Pub Date : 2020-09-11 DOI: 10.1145/3429889.3430078

Huanchen Cai, Jiangping Kong

引用次数: 1

Study on DRGs of single disease based on drug cost of patients with primary liver cancer 基于原发性肝癌患者药物费用的单一疾病DRGs研究

Proceedings of the 1st International Symposium on Artificial Intelligence in Medical Sciences Pub Date : 2020-09-11 DOI: 10.1145/3429889.3430086

Qiong Zhong, Zhihui Huang, Chi Chen, Feifei Chen, Hui Wang, Xiaomin Zhu

{"title":"Study on DRGs of single disease based on drug cost of patients with primary liver cancer","authors":"Qiong Zhong, Zhihui Huang, Chi Chen, Feifei Chen, Hui Wang, Xiaomin Zhu","doi":"10.1145/3429889.3430086","DOIUrl":"https://doi.org/10.1145/3429889.3430086","url":null,"abstract":"Objective: To explore disease diagnosis-related groupings and drug cost standards suitable for primary liver cancer patients' drug costs, and to provide recommendations for the payment mechanism of drug costs under the medical insurance system. Methods: The single factor and multiple linear regression method were used to analyze the external factors affecting the drug cost of patients with primary liver cancer, select the influencing factors with significant influence, and use the CHAID algorithm in the decision tree to establish the relevant case combination model. Results: The ratio of male to female patients with primary liver cancer is roughly 2 to 4: 1. The cost of drugs for primary liver cancer patients is covered by insurance type, actual number of hospital stays, the presence of comorbidities and accompanying diseases, the presence of TCM treatment, and the number of hospitalizations Various factors. Using the decision tree CHAID algorithm, nine disease diagnosis-related groups were obtained. Conclusion: The relevant factors reminded by single factor and multi-factor analysis can be used to explain the source of some special drug costs. In order to control drug costs more reasonably, it is necessary to improve the mechanism of the price of medical services.","PeriodicalId":315899,"journal":{"name":"Proceedings of the 1st International Symposium on Artificial Intelligence in Medical Sciences","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129035641","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

Deep Learning on Parkinson's Rat behavior Point Sets in Open Field 帕金森大鼠开放性行为点集的深度学习研究

Proceedings of the 1st International Symposium on Artificial Intelligence in Medical Sciences Pub Date : 2020-09-11 DOI: 10.1145/3429889.3429912

Lei Wu, Quan He, Hezhi Cao, Long Liu

{"title":"Deep Learning on Parkinson's Rat behavior Point Sets in Open Field","authors":"Lei Wu, Quan He, Hezhi Cao, Long Liu","doi":"10.1145/3429889.3429912","DOIUrl":"https://doi.org/10.1145/3429889.3429912","url":null,"abstract":"Parkinson's disease (PD) is one of the common progressive neurodegenerative disorder with motor deficits. A substantial volume of research has reported various methods to characterize Parkinson's rat model, but the performances are unsatisfactory. Because of the complexity of behavior data, we can hardly recognize the non-trivial behavioral traits and distinguish the difference between ill rat and the healthy. In this paper, we construct a deep learning model to analyze PD rat behavior data, which is obtained from an optical motion capture system. This system is widely used in movement analysis, robotics, making animations, and can provide high resolution of spatiotemporal information. By using the three-dimensional motion capture technology with retro-reflective optical markers placed in locations that represents rat head and body axis, we can get real-time three-dimensional coordinate information of rat's motion in open field. After the preprocessing, we fed the data set to our model to get a global feature via three abstraction layers and then process them by fully connected layers to obtain the final classification result. The results have shown that this method is robust enough to identify biomechanical and kinematic changes in response to Parkinson's rat with unilateral Striatum lesion.","PeriodicalId":315899,"journal":{"name":"Proceedings of the 1st International Symposium on Artificial Intelligence in Medical Sciences","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129460516","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

The Optimal Wavelet Basis for Electroencephalogram Denoising 脑电图去噪的最佳小波基

Proceedings of the 1st International Symposium on Artificial Intelligence in Medical Sciences Pub Date : 2020-09-11 DOI: 10.1145/3429889.3429906

Liwei Cheng, Duanling Li, Gongjing Yu, Zhonghai Zhang, Shuyue Yu

引用次数: 0

Cancer Classification and Gene Selection with Machine Learning Method 基于机器学习方法的癌症分类和基因选择

Proceedings of the 1st International Symposium on Artificial Intelligence in Medical Sciences Pub Date : 2020-09-11 DOI: 10.1145/3429889.3429913

Qin Jiang

引用次数: 1