Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences最新文献_第2页

Mental Health Question and Answering System Based on Bert Model and Knowledge Graph Technology 基于Bert模型和知识图技术的心理健康问答系统

Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences Pub Date : 2021-10-29 DOI: 10.1145/3500931.3501011

Chaohui Guo, Shaofu Lin, Zhisheng Huang, Yahong Yao

{"title":"Mental Health Question and Answering System Based on Bert Model and Knowledge Graph Technology","authors":"Chaohui Guo, Shaofu Lin, Zhisheng Huang, Yahong Yao","doi":"10.1145/3500931.3501011","DOIUrl":"https://doi.org/10.1145/3500931.3501011","url":null,"abstract":"With the development and progress of society, people are facing increasing pressure. The emergence of this phenomenon has led to a rapid increase in the incidence of mental illness. In order to deal with this phenomenon, this paper proposes a system of question and answering on the basic knowledge of mental health (MHQ&A) by using deep learning retrieval technology and knowledge graph technology. The system MHQ&A is designed mainly for the general public, to answer the basic knowledge of mental health, especially the field of depression. First of all, the basic and the professional question and answer data about mental health were respectively obtained by the reptilian bot from the \"IASK\" website knowledge and the \"Dr. Dingxiang\" website. Then, the questions and answers obtained through the crawler are made into a Question and Answering Knowledge Graph of Basic Health Knowledge in the mental health field, which is combined with semantic data of antidepressants and the semantic data of depression papers. Finally, a set of template matching rules is designed to determine the type of problem of users. If the questions are about the professional knowledge of medicine or thesis, the reasoning template will be used to reason and search the answer in the \"Question and Answering Knowledge Graph of Basic Health Knowledge in the Mental Health Field\". If the questions are about other basic knowledge in the field of mental health, the BERT model is used to vectorize the questions of users, and the matching questions and corresponding answers in the MHQ&A are found through cosine similarity calculation. Through the test of system accuracy, it is proved that the system can effectively combine deep learning technology and knowledge.","PeriodicalId":364880,"journal":{"name":"Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128257049","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

Influence of BMI on Walking Health-Based on the Analysis of Gait Parameters BMI对步行健康的影响——基于步态参数分析

Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences Pub Date : 2021-10-29 DOI: 10.1145/3500931.3501013

M. Chang, Zumin Wang, Shuai Tao

{"title":"Influence of BMI on Walking Health-Based on the Analysis of Gait Parameters","authors":"M. Chang, Zumin Wang, Shuai Tao","doi":"10.1145/3500931.3501013","DOIUrl":"https://doi.org/10.1145/3500931.3501013","url":null,"abstract":"Aim: By comparing the differences in gait parameters of subjects with different body mass indexes (BMI) in the four states, this paper aims to explore the influence of BMI on gait. Method: Fifty-two subjects with different BMI are divided into the normal and obese groups, and their gait parameters under four states are collected. The data is analyzed and processed by SPSS24. Result: 1) When walking in a natural state with open or closed eyes, the obese group has a greater stance phase and less swing phase than those of the normal group (P<0.05), and has lower braking force than that of the other group (P<0.01); 2) When walking in the thinking state with open eyes, the obese group has a lower braking force and greater stride variability than those of the normal group (P<0.05). Conclusions: 1) Subjects with a large BMI have low symmetry and stability under a high risk of falling; 2) Subjects with large BMI have a low braking force and decreased physical function; 3) Subjects with a large BMI value have greater stance phase and less swing phase, indicating that they are afraid of falling when walking (panic gait); 4) Vision and concentration are important factors that affect gait.","PeriodicalId":364880,"journal":{"name":"Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences","volume":"6 23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129669094","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

Analysis and prediction of COVID-19 trends in the United States 美国COVID-19趋势分析与预测

Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences Pub Date : 2021-10-29 DOI: 10.1145/3500931.3501008

Zhengyang Guo, Wenqi Yang, T. Yan, Y. Lin, Jiawei Wang

引用次数: 0

Magnetic resonance imaging of adolescent depression based on machine vision 基于机器视觉的青少年抑郁症磁共振成像

Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences Pub Date : 2021-10-29 DOI: 10.1145/3500931.3500996

Xingyu Chen, Yusen Zhang

{"title":"Magnetic resonance imaging of adolescent depression based on machine vision","authors":"Xingyu Chen, Yusen Zhang","doi":"10.1145/3500931.3500996","DOIUrl":"https://doi.org/10.1145/3500931.3500996","url":null,"abstract":"Magnetic resonance imaging (MRI) is emerging as a significant technique for understanding the neuropathological mechanisms and clinical identification of mild depression disorders (MDD). Adolescent depression is a typical class of psychiatric disorders. The number of patients keeps increasing these years. Though some studies have shown pathological changes in adult depression, the study in adolescent depression is still limited. This review discussed the application of MRI techniques in adolescent depression. It summarized the abnormity between adolescent depression and healthy controls to help clinical diagnosis and adolescent depression. Structure magnetic resonance imaging (sMRI) and function magnetic resonance imaging (fMRI) effectively analyse adolescent depression. However, diffusion tensor imaging (DTI) was indicated that could find further abnormity of adolescent depression. Future research is needed to adopt a more advanced MRI methodology in the diagnosis and treatment of MDD. Besides, With the development of artificial intelligence, computer image processing technology is more closely combined with medical science. Here we discussed the application of MRI technology in adolescent depression by combining MRI technology under computer image processing technology, in order to provide better help for the analysis of the disease.","PeriodicalId":364880,"journal":{"name":"Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133700671","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

Optimization of Nucleic Acid Detection Method under the New Epidemic Situation in Macao based on Experimental and Mathematical Statistics Analysis 基于实验与数理统计分析的澳门新疫情下核酸检测方法优化

Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences Pub Date : 2021-10-29 DOI: 10.1145/3500931.3501020

Kam Kuan Ao, Cheng Hon Lin, Dong Li

{"title":"Optimization of Nucleic Acid Detection Method under the New Epidemic Situation in Macao based on Experimental and Mathematical Statistics Analysis","authors":"Kam Kuan Ao, Cheng Hon Lin, Dong Li","doi":"10.1145/3500931.3501020","DOIUrl":"https://doi.org/10.1145/3500931.3501020","url":null,"abstract":"A large number of COVID-19 nucleic acid tests are performed every day in the world. If the testing is performed individually, the testing workload and testing cost would be high. In this paper, we proposed a simulated model to check the efficiency of group testing methods given four typical situations and find the best grouping number for different predicted infection rates. Maximum likelihood Estimation (MLE) is applied for model estimation and Inversion Method is used to generate pseudo random variables for simulation. The simulation results point to the conclusions: (1) Given a high infection rate (p>0.1), both the false-positive and the false-negative possibilities have a poor relation with the grouping number. (2) However, given a low infection rate (p<0.1), the false-positive and false-negative possibilities will become important factors to reduce the grouping number. Especially, if p is close to 0.01, the grouping number is significantly affected. (3) With a 10% infection rate, it is recommended to group 5 participants in one test given no false-positive or false-negative situations. (4) If p<0.3, the group testing approach has the potential to improve overall testing efficiency.","PeriodicalId":364880,"journal":{"name":"Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134108744","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

Transfer Learning based Recognition Algorithm for Common Tea Disease 基于迁移学习的常见茶病识别算法

Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences Pub Date : 2021-10-29 DOI: 10.1145/3500931.3500994

Bo Tian

引用次数: 1

Development of Scale Test Platform for Behavioral Medicine Using Artificial Intelligence Method to Accelerate the Computerization of Behavioral Medicine Scale 用人工智能方法开发行为医学量表测试平台，加速行为医学量表的计算机化

Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences Pub Date : 2021-10-29 DOI: 10.1145/3500931.3500958

Chenyu Wang, Yifan Wang, Zhiwei Feng, Rongjuan Guo, Jialin Wang, Jia Xing, Chongyun Zhou

{"title":"Development of Scale Test Platform for Behavioral Medicine Using Artificial Intelligence Method to Accelerate the Computerization of Behavioral Medicine Scale","authors":"Chenyu Wang, Yifan Wang, Zhiwei Feng, Rongjuan Guo, Jialin Wang, Jia Xing, Chongyun Zhou","doi":"10.1145/3500931.3500958","DOIUrl":"https://doi.org/10.1145/3500931.3500958","url":null,"abstract":"The scale is a general rule summarized in a large number of clinical processes, and the professional medical scale is statistically scientific. In the medical field, scales are very important to the clinic, and many scales are used clinically to evaluate the severity and prognosis of diseases. The development of the scale requires a lot of effort, but the use of the traditional scale requires doctors to spend high learning costs to use and diagnose. Therefore, it is particularly important to develop an easy-to-use artificial intelligence-based intelligent testing platform for behavioral medicine scales. We analyze the behavioral medicine scale's norms (cutoff values), implementation procedures, assignments, scoring, score conversion, factors and conclusions and other attributes, and use object-oriented programming ideas to model behavioral medicine scales. Finally, according to this model, the behavioral medicine scale intelligent test platform is realized through C++ programming. Developed a test platform with multiple functions such as scale evaluation, scale import, scale management, user management, scale report reviews and printing, historical evaluation record statistical analysis and other functions, and it was carried out through the cerebrovascular disease scale and nursing scale verify. The platform has a certain general purpose and can be widely used in the development, verification and clinical application of the behavioral medicine scale assessment.","PeriodicalId":364880,"journal":{"name":"Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences","volume":"12 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122621911","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

Optimization of Conditions for Ginsenoside Re liposomes Production by Response Surface Methodology 响应面法优化人参皂苷Re脂质体生产条件

Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences Pub Date : 2021-10-29 DOI: 10.1145/3500931.3500983

Yujie Chen, Rongfa Guan, Haizhi Huang, Hao Zhong, Yujing Sun, Wei Zhang

引用次数: 0

Classification and prediction model of compound pharmacokinetic properties based on ensemble learning method 基于集成学习方法的化合物药代动力学性质分类与预测模型

Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences Pub Date : 2021-10-29 DOI: 10.1145/3500931.3501021

Jiayi Zhao, Yang Liu

引用次数: 1

Research on Internet False Advertising Monitoring Technology in TCM Medical Institutions 中医医疗机构网络虚假广告监控技术研究

Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences Pub Date : 2021-10-29 DOI: 10.1145/3500931.3501012

Rui Xiao, Fengju Hu, Wei Pei, Biao Guang

引用次数: 0