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

{"title":"Research on Tibetan medicine intelligent question answering system integrating confrontation training and reinforcement learning","authors":"Andi Dong, Chao Wang, Pan Tong, Dan Yang, Cuo Yong","doi":"10.1145/3500931.3500953","DOIUrl":"https://doi.org/10.1145/3500931.3500953","url":null,"abstract":"In this study, a knowledge graph (KG) based Tibetan medicine intelligent question answering (QA) system model was proposed based on an adversarial learning generative network model, in an attempt to alleviate the scarcity of medical resources, promote the heritage and innovation of Tibetan medicine, and ease the shortage of Tibetan medical information. In this model, the simulated answers were generated via adversarial learning, and subsequently the reinforcement learning was applied for feedback-based optimization, with the ultimate aim of enhancing the accuracy rate of this model. Besides, a triple extraction method based on Tibetan features was proposed to construct a KG dialog set. Finally, this model was subjected to an experiment in Chinese and Tibetan datasets, with the results indicating that the accuracy of this intelligent QA model incorporating adversarial networks and reinforcement learning was higher than other models.","PeriodicalId":364880,"journal":{"name":"Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences","volume":"21 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":"133405896","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}

{"title":"Research on Collaborative Quality Assessment Model of Elbow Muscles based on MC-MMG and DRSN","authors":"W. Lu, Lifu Gao, Huibin Cao, Jianying Zhang, Z. Li, Daqing Wang","doi":"10.1145/3500931.3501010","DOIUrl":"https://doi.org/10.1145/3500931.3501010","url":null,"abstract":"The purpose of our study was to investigate the individual muscle contribution to generated force under four representative of elbow multi-muscle contraction tasks: flexion, extension, pronation, and supination. In this paper, we proposed a collaborative quality assessment model of muscles to elbow generated force based on a multi-channel mechanomyogram (MC-MMG) to explore the relationship between the elbow generated force and the individual muscles under different contraction tasks. Based on the analysis of elbow anatomy, MMG signals of brachial biceps (BB), brachial (BR), triceps (TR), brachioradialis (BRD) were collected by using MC-MMG collection platform. The Kernel Principal Component Analysis (KPCA) algorithm was used to reduce the dimension of the original MMG signal. Then, the Mean Average Value (MAV) feature of the signals was extracted as the input of the Deep Residual Shrinkage Network (DRSN), which is a new deep learning algorithm to establish the relationship between MC-MMG and generated force. Mean Impact Value (MIV) index was used to assess the contribution level of different muscles groups for estimating the generated force. The experimental results show that the single muscle with the highest MIV value can track the change of generated force better than multiple muscles under different contraction tasks. This result can provide effective guidance for estimating generated force and can be further applied to the recognition of motion intention.","PeriodicalId":364880,"journal":{"name":"Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences","volume":"2 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":"129082448","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}

{"title":"Study on the relationship between BDNF and electroacupuncture analgesia under the background of Intelligent Technology","authors":"Renjun Cai, Fengyuan Bai, Sufei Song, Dongmei Zhao, Tao Liu, Qiu-ling Xu","doi":"10.1145/3500931.3500949","DOIUrl":"https://doi.org/10.1145/3500931.3500949","url":null,"abstract":"Chronic pain is a serious threat to human health. Studies have shown that Brain-derived neurotrophic factor (BDNF) is closely related to pain and plays a crucial role in the induction to chronic pain. At present, the clinical treatment of neuropathic pain is mainly through drug treatment, but there are many adverse reactions, which limits their wide application. In recent years, electroacupuncture has been widely studied in the treatment of chronic pain, and has few adverse reactions. So it is worthy of application and promotion. Research conclusions show that electroacupuncture (EA) can effectively inhibit pain, promote the expression of BNDF and play an analgesic effect. At present, the combination of acupuncture treatment and intelligence technology is a hot topic, and it is also the trend of acupuncture treatment for the future. Today, with the rapid development of intelligent technology, we should make good use of the combination of intelligent technology and acupuncture analgesia to give better play to the advantages of acupuncture analgesia. This paper reviews the relationship between BDNF and electroacupuncture analgesia under the background of intelligent technology.","PeriodicalId":364880,"journal":{"name":"Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences","volume":"44 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":"116027221","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}

{"title":"Research Trends and Hotspot Visualization Analysis of \"Internet + Nursing Services\" in China from 2010 to 2021","authors":"Yanfei Chen, Shihua Cao, Yuchao Le, Danni He, Mengxin Wang, Beiying Qian","doi":"10.1145/3500931.3500971","DOIUrl":"https://doi.org/10.1145/3500931.3500971","url":null,"abstract":"This paper is to analyze the current status of research on \"Internet + nursing services\" in China in the past ten years and the frontiers of research hotspots to provide a reference for the better development of Internet and nursing services in the future. The method adopted in this paper is to search relevant literatures in the field of \"Internet + nursing services\" in China from 2010 to 2021 in the database of China National Knowledge Network (CNKI), download the data to be analyzed, and use the Cite Space visualization tool to analyze the authors, research institutions, keywords, etc. Finally, 718 valid documents were obtained, showing an increasing trend of annual publication; the highest number of articles by authors was 6; the highest number of articles by research institutions was 5. The high-frequency keywords include \"Internet+\", \"nursing service\", \"extended care\", \"home care \"etc. The conclusion is that \"Internet + nursing services\" in China is currently in a rapid development stage, and although some results have been achieved, there is still a lack of more academic authors and more authoritative institutions, and further in-depth research is needed in the coming period.","PeriodicalId":364880,"journal":{"name":"Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences","volume":"6 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":"115336236","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}

{"title":"Network pharmacology-based research on the action mechanism of Caulis Sinomenii in treating rheumatoid arthritis","authors":"Yuzhi Shang, Chenling Li, Qinghuai Zhang, A. Hang, Gang Fang, Yuzhou Pang","doi":"10.1145/3500931.3500982","DOIUrl":"https://doi.org/10.1145/3500931.3500982","url":null,"abstract":"Caulis sinomenii (CS) is one of the main herbs for the treatment of rheumatoid arthritis (RA) in the southwestern minority areas of China. However, there are multiple components in CS, and their synergy in treating RA is still not clear. In this study, we aimed to explore action mechanism of CS in treating RA. The CS component was obtained by TCMSP and ETCM, and the active component of CS was screened by reference to oral bioavailability and drug-like properties. We obtained the target of RA through DisGeNET and CTD, and mapped the target of CS active component with disease target by BATMAN-TCM to obtain the potential target of CS treatment of RA. Next, we performed GO and KEGG pathway enrichment analysis on the potential targets of CS treatment of RA, and constructed a network of interactions between targets, CS-active component-target-critical pathway networks. Finally, we referred to the network's topological parameters, KEGG pathway annotation information, etc. to screen and analyze target points, GO terms, and pathways, and used molecular docking technology to verify the selected key targets. We obtained 9 active components of CS (Beta-sitosterol, Stigmasterol, Stepholidine, etc.) and 30 potential targets for CS treatment of RA (AKT1, NFKB1, JUN, etc.). In the results of enrichment of these targets, 20 key pathways (Osteoclast differentiation, Toll-like receptor signaling pathway, Leukocyte transendothelial migration, etc.) and more than 160 GO entries (lysosome, cell surface, NADPH oxidase complex, etc.) were screened. We constructed CS-active component-target-key pathway network to visually demonstrate the relationships between the various levels. We screened 7 hub targets in the network, and the molecular docking results of the 7 Hub target-corresponding protein and CS active components showed strong binding ability. In this study, we predicted the multi-component and multi-target synergy of CS in treating RA, and provided a reference for further experimental verification and clinical application.","PeriodicalId":364880,"journal":{"name":"Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences","volume":"110 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":"115744314","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}

{"title":"Flexible temperature Sensor based on PDMS/CNT/Ti3C2TX for physiological temperature monitoring","authors":"Jingshu Yang","doi":"10.1145/3500931.3500987","DOIUrl":"https://doi.org/10.1145/3500931.3500987","url":null,"abstract":"The flexible temperature sensor for physiological temperature monitoring based on polydimethylsiloxane/carbon nanotubes/Ti3C2Tx (PDMS/CNT/Ti3C2TX) was prepared. The thermosensitive layer was made by filling different proportions of grind Ti3C2Tx into PDMS/CNT (PC). PDMS thermal expansion and the thermal properties of the conductive network inside dominated the sensor performance. The experimental results showed that the sensitivity of PDMS/CNT/Ti3C2Tx temperature sensor (PCT-TS) decreased by 50% compared with PDMS/CNT10%, (PC10) when the mass of Ti3C2Tx was 10%, and the resistance still showed a negative temperature coefficient. However, the temperature coefficient reversed by further increasing the Ti3CTx mass to 30%. The sensitivity of Ti3C2Tx was greatly improved by approximately 3 times better than PC10. After that, excessive Ti3C2Tx only increased the density of the conductive path, which inhibited the deformation of the internal conductive network and the flexibility of the material. PCT-TS has the advantages of adjustable temperature coefficients, high sensitivity, and simple manufacturing methods, which are expected to be applied in human medicine and health detection.","PeriodicalId":364880,"journal":{"name":"Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences","volume":"16 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":"127793696","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}

{"title":"Artificial Intelligence Based Myocardial Ischemia Detection in Cardiac Radiology","authors":"Xiangru Li, J. Tian, N. Nan, C. Tu, Dongfeng Zhang, Xiantao Song, Hongjia Zhang","doi":"10.1145/3500931.3500951","DOIUrl":"https://doi.org/10.1145/3500931.3500951","url":null,"abstract":"Artifical intelligence (AI) is changing many aspects of our lives. It includes machine learning (ML) and deep learning (DL), which can automatically obtain information from the database through algorithms and make predictions. It has been widely used in medicine, especially in the cardiovascular field. In this review, we made a brief overview of AI, as well as the application of AI in the cardiac radiology to noninvasively detect myocardial ischemia. At the same time, we summarized the current limitations and prospects of AI in the field of myocardial ischemia. Finally, AI can accomplish the task of image recognition very well, and make accurate judgments with the same effectiveness as cardiologists or imaging experts.","PeriodicalId":364880,"journal":{"name":"Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences","volume":"1 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":"125886860","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}

{"title":"MDepressionKG","authors":"Chengcheng Fu, Xiaobin Jiang, Tingting He, Xingpeng Jiang","doi":"10.1145/3500931.3500944","DOIUrl":"https://doi.org/10.1145/3500931.3500944","url":null,"abstract":"Depression, as a global psychological disorder, is one of the important factors that cause human health, economic or social burden. Researches have shown that metabolism disorders caused by immune system diseases (i.e. diabetes, crohn disease, irritable bowel syndrome) are closely related to depression. There are large numbers of microbes in human micro-ecological environment. The metabolites of these microbes can also affect as the neurochemical and inflammatory factors in the human brain through the human brain-gut axis, which further affect the emergence of depression. In recent years, researches on the association between microbial metabolism and depression have been published in scientific literature, Wikipedia pages and other biological databases. But few efforts have been made to curate them as structured knowledge, which will make more convenient for the biological and medical community. In this research, we propose and construct a model of knowledge graph linking all metabolism entities of human and their microbes to depression disorder (called MDepressionKG). MDepressionKG has the following advantages: (1) It integrates the human microbial metabolism network, human diseases, microbes and other fields ontologies. (2) The knowledge graph provides a semantic-based logical reasoning for generating potential associations automatically. (3) Various applications such as the discovery of depression comorbidities can be applied as case studies to provide explorations for further depression intervention. The friendly interactive platform for knowledge retrieval and visualization, which is freely available at the URL at http://microbekg.msbio.pro/explore/MDepressionKG.","PeriodicalId":364880,"journal":{"name":"Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences","volume":"16 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":"124156458","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}

{"title":"KGBReF","authors":"Yueping Sun, Zhisheng Huang, Jiao Li, Zidu Xu, Li Hou","doi":"10.1145/3500931.3500952","DOIUrl":"https://doi.org/10.1145/3500931.3500952","url":null,"abstract":"With the rapid development of bibliographical data of biomedical articles, it is hard for scientists to keep up with the most recent biomedical literatures. Biomedical relation extraction aims to uncover high-quality relations from biomedical literature with high accuracy and efficiency. Of the existing text mining tools and semantic web products for relation extraction, knowledge graph, a large scale semantic network consisting of entities and concepts as well as the semantic relations among them, has enriched information for human annotation and thus has a great potential for assisting the extraction of the new relations. In this paper, we propose a knowledge graph based biomedical relation extraction framework KGBReF and apply the framework to explore emotion-probiotic relations. A probiotics knowledge graph with 40, 442, 404 triples was built and candidate relations in totally 1,453 PubMed articles were further retrieved by reasoning and annotated. Further, the evidence levels of relations were retrieved and visualized. Finally, we got an evidenced emotion-probiotic relation graph. KGBReF demonstrates an effective reasoning based framework of relation extraction by defining top concepts only. The annotated relation associations are supposed be used to help researchers generate scientific hypotheses or create their own semantic graphs for their research interests.","PeriodicalId":364880,"journal":{"name":"Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences","volume":"31 1 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":"124321887","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}

{"title":"Automatic diagnosis of multiple lesions in fundus images based on dual attention mechanism","authors":"Jiamin Gong, Liufei Guo, Jiewei Jiang, Che-Ming Wu, Mengjie Pei, Wei Liu","doi":"10.1145/3500931.3500975","DOIUrl":"https://doi.org/10.1145/3500931.3500975","url":null,"abstract":"Glaucomatous optic neuropathy (GON), retinal exudates and retinal hemorrhage are the main basis for the diagnosis of fundus diseases. Traditional methods can diagnose fundus diseases and their severity, but there are few studies on the characteristics of fundus diseases, which cannot give a reasonable explanation for the diagnosis of fundus diseases. Therefore, a convolutional neural network based on dual attention mechanism was proposed to realize automatic diagnosis of multiple fundus lesions with high accuracy. Convolutional neural network uses a residual structure with jumping connections, and channels and spatial attention mechanisms are embedded after each group of convolution to improve the accuracy of fundus lesions diagnosis. The model was tested on the clinical data of Ningbo Eye Hospital Affiliated to Wenzhou Medical University. The diagnostic accuracy of GON, retinal exudates and retinal hemorrhage were 98.17%, 97.49% and 97.15%, respectively. The experimental results showed that: the model showed good feature extraction ability and diagnostic performance in multi-lesion diagnosis of fundus, which provided reference value for subsequent medical artificial intelligence diagnosis research.","PeriodicalId":364880,"journal":{"name":"Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences","volume":"49 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":"121847722","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}