发布求助
文献互助 智能选刊 最新文献

Journal of Artificial Intelligence for Medical Sciences最新文献

筛选
英文 中文
Temporal Aspects of Tree Hole Data 树洞数据的时间方面
Journal of Artificial Intelligence for Medical Sciences Pub Date : 2021-06-01 DOI: 10.2991/JAIMS.D.210604.001
Zengzhen Du, D. Xie, Min-Shuo Hu
{"title":"Temporal Aspects of Tree Hole Data","authors":"Zengzhen Du, D. Xie, Min-Shuo Hu","doi":"10.2991/JAIMS.D.210604.001","DOIUrl":"https://doi.org/10.2991/JAIMS.D.210604.001","url":null,"abstract":"At present, adolescent suicide becomes a serious social problem. Many young people express suicidal thoughts through online socialmedia.Weibo is a famous socialmedia platform for real-time information sharing inChina.When aWeibo user committed suicide, many other users continued to post information on this Weibo. Such a space is often called a “tree hole.” By analyzing the temporal aspects of tree hole data, we can understand the behavioral characteristics of suicide attempters and provide more valuable information for suicide assistance. This paper will introduce the analysis of temporal characteristics of tree hole data and guide suicide assistance through suicide monitoring and early warning based on these time characteristics.","PeriodicalId":196434,"journal":{"name":"Journal of Artificial Intelligence for Medical Sciences","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128081748","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
RadCloud—An Artificial Intelligence-Based Research Platform Integrating Machine Learning-Based Radiomics, Deep Learning, and Data Management radcloud -基于人工智能的研究平台，集成了基于机器学习的放射组学、深度学习和数据管理
Journal of Artificial Intelligence for Medical Sciences Pub Date : 2021-06-01 DOI: 10.2991/jaims.d.210617.001
Geng Yayuan, Zhang Fengyan, Zhang Ran, Chen Ying, Xiang Yuwei, Wang Fang, Yang Xunhong, Zuo Panli, Chai Xiangfei
{"title":"RadCloud—An Artificial Intelligence-Based Research Platform Integrating Machine Learning-Based Radiomics, Deep Learning, and Data Management","authors":"Geng Yayuan, Zhang Fengyan, Zhang Ran, Chen Ying, Xiang Yuwei, Wang Fang, Yang Xunhong, Zuo Panli, Chai Xiangfei","doi":"10.2991/jaims.d.210617.001","DOIUrl":"https://doi.org/10.2991/jaims.d.210617.001","url":null,"abstract":"Radiomics and artificial intelligence (AI) are two rapidly advancing techniques in precision medicine for the purpose of dis- ease diagnosis, prognosis, surveillance, and personalized therapy. This paper introduces RadCloud, an artificial intelligent (AI) research platform that supports clinical studies. It integrates machine learning (ML)-based radiomics, deep learning (DL), and data management to simplify AI-based research, supporting rapid introduction of AI algorithms across various medical imaging specialties tomeettheever-increasingdemandsoffutureclinical research.Thisplatform hasbeen successfullyappliedfortumor detection, biomarker identification, prognosis, and treatment effect assessment across various image modalities (MR, PET/CT, CTA, US, MG, etc.) and a variety of organs (breast, lung, kidney, liver, rectum, thyroid, bone, etc). The proposed platform has shown great potential in supporting clinical studies for precision medicine.","PeriodicalId":196434,"journal":{"name":"Journal of Artificial Intelligence for Medical Sciences","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114350088","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
Ensembled Deep Neural Network for Intracranial Hemorrhage Detection and Subtype Classification on Noncontrast CT Images 基于集成深度神经网络的颅内出血非对比CT图像检测及亚型分类
Journal of Artificial Intelligence for Medical Sciences Pub Date : 2021-06-01 DOI: 10.2991/jaims.d.210618.001
Yunan Wu, M. Supanich, Jie Deng
{"title":"Ensembled Deep Neural Network for Intracranial Hemorrhage Detection and Subtype Classification on Noncontrast CT Images","authors":"Yunan Wu, M. Supanich, Jie Deng","doi":"10.2991/jaims.d.210618.001","DOIUrl":"https://doi.org/10.2991/jaims.d.210618.001","url":null,"abstract":"Rapid and accurate diagnosis of intracranial hemorrhage is clinically significant to ensure timely treatment. In this study, we developedanensembleddeepneuralnetworkforthedetectionandsubtypeclassificationofintracranialhemorrhage.Themodelconsistedoftwoparallelnetworkpathways,oneusingthreedifferentwindowlevel/widthsettingstoenhancetheimagecon-trastofbrain,blood,andsofttissue.Theotherextractedspatialinformationofadjacentimageslicestothetargetslice.BothpathwaysexploitedtheEfficientNet-B0asthebasicarchitectureandwereensembledtogeneratethefinalprediction.Classacti-vationmappingwasappliedinbothpathwaystohighlighttheregionsofdetectedhemorrhageandtheassociatedsubtypes.ThemodelwastrainedandtestedusingIntracranialHemorrhageDetectionChallenge(IHDC)datasetlaunchedbytheRadiologicalSocietyofNorthAmerica(RSNA)in2019,whichcontained674,258headnoncontrastscomputertomographyimagesacquiredfrom19,530patients.Anindependentdataset(CQ500)acquiredfromanotherinstitutionwasusedtotestthegeneralizabilityofthetrainedmodel.Theoverallaccuracy,sensitivity,andF1scoreforintracranialhemorrhagedetectionwere95.7%,85.9%,and86.7%onIHDCtestingdatasetand92.4%,92.6%,and93.4%onexternalCQ500testingdataset.Theheatmapsbyclassacti-vationmappingsuccessfullydemonstrateddiscriminativefeatureregionsofthepredictedhemorrhagelocationsandsubtypes,providingvisualguidanceforradiologiststoassistinrapiddiagnosisofintracranialhemorrhage.","PeriodicalId":196434,"journal":{"name":"Journal of Artificial Intelligence for Medical Sciences","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129904187","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: 6
Exploring Medical Students' and Faculty's Perception on Artificial Intelligence and Robotics. A Questionnaire Survey 医学生和教师对人工智能和机器人的认知探讨。问卷调查
Journal of Artificial Intelligence for Medical Sciences Pub Date : 2021-06-01 DOI: 10.2991/jaims.d.210617.002
L. Sassis, P. Kefala-Karli, Marina Sassi, C. Zervides
{"title":"Exploring Medical Students' and Faculty's Perception on Artificial Intelligence and Robotics. A Questionnaire Survey","authors":"L. Sassis, P. Kefala-Karli, Marina Sassi, C. Zervides","doi":"10.2991/jaims.d.210617.002","DOIUrl":"https://doi.org/10.2991/jaims.d.210617.002","url":null,"abstract":"Over the last decade, the emerging fields of artificial intelligence (AI) and robotics have been introduced in medicine, gaining much attention. This study aims to assess the insight of medical students and faculty regarding AI and robotics in medicine. A cross-sectional study was conducted among medical students and faculty of the University of Nicosia. An online questionnaire was used to evaluate medical students’ and faculty’s prior knowledge and perceptions toward AI and robotics. Data analysis was carried out using SPSS software, and the statistical significance was assumed as p value < 0.05. Three hundred eighty-seven medical students and 23 faculty responded to the questionnaire. Students who were “familiar” with AI and robotics stated that these breakthrough technologies make them more enthusiastic about working in their speciality of interest ( p value = 0.012). Also, students (59.9%) and faculty (47.8%) agreed that physician’s opinion should be followed when doctors’ and AI’s judgment differandthatthedoctorinchargeshouldbeliableforpossibleAI’smistakes(38.8%students:47.7%faculty).AlthoughthemostsignificantdrawbackofAIandroboticsinhealthcareisthedehumanizationofmedicine(54.5%students;47.8%faculty),most participants(77.6%students;78.2%faculty)agreedthatmedicalschoolsshouldincludeintheircurriculumAIandroboticsby offeringrelevantcourses(39.5%students;52.2%faculty).Medicalstudentsandfacultyarenotanxiousabouttheadvancements ofAIandroboticsinmedicine.MedicalschoolsshouldtaketheleadandintroduceAIandroboticsinundergraduatemedicalcurriculabecausetheneweraneedsfullyawarehealthcareproviderswithbetterinsightregardingtheseconcepts. © 2021 The Authors . Published by Atlantis Press B.V. This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).","PeriodicalId":196434,"journal":{"name":"Journal of Artificial Intelligence for Medical Sciences","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130336971","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: 5
TMRGM: A Template-Based Multi-Attention Model for X-Ray Imaging Report Generation TMRGM:基于模板的x射线成像报告生成的多注意模型
Journal of Artificial Intelligence for Medical Sciences Pub Date : 2021-05-01 DOI: 10.2991/JAIMS.D.210428.002
Xuwen Wang, Yu Zhang, Zhen Guo, Jiao Li
{"title":"TMRGM: A Template-Based Multi-Attention Model for X-Ray Imaging Report Generation","authors":"Xuwen Wang, Yu Zhang, Zhen Guo, Jiao Li","doi":"10.2991/JAIMS.D.210428.002","DOIUrl":"https://doi.org/10.2991/JAIMS.D.210428.002","url":null,"abstract":"The rapid growth of medical imaging data brings heavy pressure to radiologists for imaging diagnosis and report writing. This paper aims to extract valuable information automatically from medical images to assist doctors in chest X-ray image interpretation. Considering the different linguistic and visual characteristics in reports of different crowds, we proposed a template-based multi-attention report generation model (TMRGM) for the healthy individuals and abnormal ones respectively. In this study, we developed an experimental dataset based on the IU X-ray collection to validate the effectiveness of TMRGM model. Specifically, our method achieves the BLEU-1 of 0.419, the METEOR of 0.183, the ROUGE score of 0.280, and the CIDEr of 0.359, which are comparable with the SOTA models. The experimental results indicate that the proposed TMRGM model is able to simulate the reporting process, and there is still much room for improvement in clinical application.","PeriodicalId":196434,"journal":{"name":"Journal of Artificial Intelligence for Medical Sciences","volume":"134 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131020928","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: 3
Deep Learning Methodologies for Genomic Data Prediction: Review 基因组数据预测的深度学习方法:综述
Journal of Artificial Intelligence for Medical Sciences Pub Date : 2021-05-01 DOI: 10.2991/JAIMS.D.210512.001
Yusuf Aleshinloye Abass, Steve A. Adeshina
{"title":"Deep Learning Methodologies for Genomic Data Prediction: Review","authors":"Yusuf Aleshinloye Abass, Steve A. Adeshina","doi":"10.2991/JAIMS.D.210512.001","DOIUrl":"https://doi.org/10.2991/JAIMS.D.210512.001","url":null,"abstract":"The last few years have seen an advancement in genomic research in bioinformatics. With the introduction of high-throughput sequencing techniques, researchers now can analyze and produce a large amount of genomic datasets and this has aided the classification of genomic studies as a “big data” discipline. There is a need to develop a robust and powerful algorithm and deep learning methodologies can provide better performance accuracy than other computational methodologies. In this review, we captured the most frequently used deep learning architectures for the genomic domain. We outline the limitations of deep learning methodologies when dealing with genomic data and we conclude that advancement in deep learning methodologies will help rejuvenate genomic research and build a better architecture that will promote a genomic task.","PeriodicalId":196434,"journal":{"name":"Journal of Artificial Intelligence for Medical Sciences","volume":"280 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127477479","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: 4
Exploring the Microbiota-Gut-Brain Axis for Mental Disorders with Knowledge Graphs 用知识图谱探索精神障碍的微生物-肠-脑轴
Journal of Artificial Intelligence for Medical Sciences Pub Date : 1900-01-01 DOI: 10.2991/jaims.d.201208.001
Ting Liu, Xueli Pan, Xu Wang, K. Feenstra, J. Heringa, Zhisheng Huang
{"title":"Exploring the Microbiota-Gut-Brain Axis for Mental Disorders with Knowledge Graphs","authors":"Ting Liu, Xueli Pan, Xu Wang, K. Feenstra, J. Heringa, Zhisheng Huang","doi":"10.2991/jaims.d.201208.001","DOIUrl":"https://doi.org/10.2991/jaims.d.201208.001","url":null,"abstract":"Gut microbiota has a significant influence on brain-related diseases through the communication routes of the gut-brain axis. Manyspeciesofgutmicrobiotaproduceavarietyofneurotransmitters.Inessence,theneurotransmittersarechemicalsthatinflu-ence mood, cognition, and behavior of the host. The relationships between gut microbiota and neurotransmitters has received much attention in medical and biomedical research. However, the integration of the various proposed neurotransmitter signal routes that underpin these relationships has not yet been studied well. To unlock the influence of gut microbiota on mental health via neurotransmitters, the microbiota-gut-brain (MGB) axis, we gather the decentralized results in the existing studies into a structured knowledge base. In this paper, we therefore propose a novel Microbiota Knowledge Graph based on a newly constructed knowledge graph for uncovering the potential associations among gut microbiota, neurotransmitters, and mental disorders which we refer to as MiKG. It includes many interfaces that link to well-known biomedical ontologies, e.g. UMLS, MeSH, KEGG, and SNOMED CT, and is extendable by linking to future ontologies to further exploit the relationships between gut microbiota and neurotransmitters. This paper present MiKG, an effective knowledge graph, that can be used to investigate the MGB axis using the relationships among gut microbiota, neurotransmitters, and mental disorders.","PeriodicalId":196434,"journal":{"name":"Journal of Artificial Intelligence for Medical Sciences","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133346351","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: 13
A Method of Text Information Normalization of Electronic Medical Records of Traditional Chinese Medicine 一种中医电子病历文本信息规范化方法
Journal of Artificial Intelligence for Medical Sciences Pub Date : 1900-01-01 DOI: 10.55578/joaims.221108.001
Can Li, Dan-mei Xie
{"title":"A Method of Text Information Normalization of Electronic Medical Records of Traditional Chinese Medicine","authors":"Can Li, Dan-mei Xie","doi":"10.55578/joaims.221108.001","DOIUrl":"https://doi.org/10.55578/joaims.221108.001","url":null,"abstract":"<jats:p />","PeriodicalId":196434,"journal":{"name":"Journal of Artificial Intelligence for Medical Sciences","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117222591","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
Dynamics of Cellular Intelligence (CI) and Artificial Intelligence (AI): Health Perspectives 细胞智能 (CI) 和人工智能 (AI) 的动态：健康视角
Journal of Artificial Intelligence for Medical Sciences Pub Date : 1900-01-01 DOI: 10.55578/joaims.230522.001
Nilesh Sharma, Sachin C. Sachin C.
{"title":"Dynamics of Cellular Intelligence (CI) and Artificial Intelligence (AI): Health Perspectives","authors":"Nilesh Sharma, Sachin C. Sachin C.","doi":"10.55578/joaims.230522.001","DOIUrl":"https://doi.org/10.55578/joaims.230522.001","url":null,"abstract":"<jats:p />","PeriodicalId":196434,"journal":{"name":"Journal of Artificial Intelligence for Medical Sciences","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116342012","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 the Geospatial Characteristics of Emotional Expression in Micro-blog “Tree Hole” 微博 "树洞 "中情感表达的地理空间特征研究
Journal of Artificial Intelligence for Medical Sciences Pub Date : 1900-01-01 DOI: 10.55578/joaims.230309.001
Yahong Yao, Shao Lin, Huang Zhisheng, Ying Wu
{"title":"Research on the Geospatial Characteristics of Emotional Expression in Micro-blog “Tree Hole”","authors":"Yahong Yao, Shao Lin, Huang Zhisheng, Ying Wu","doi":"10.55578/joaims.230309.001","DOIUrl":"https://doi.org/10.55578/joaims.230309.001","url":null,"abstract":"<jats:p />","PeriodicalId":196434,"journal":{"name":"Journal of Artificial Intelligence for Medical Sciences","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127783021","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
下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信