利用公共资源的知识提取,基于表型信息的疾病表征

Gerardo Lagunes García, A. R. González
{"title":"利用公共资源的知识提取,基于表型信息的疾病表征","authors":"Gerardo Lagunes García, A. R. González","doi":"10.1109/CBMS.2019.00124","DOIUrl":null,"url":null,"abstract":"Despite the huge findings made by the study of the behaviour of diseases, there are currently many non-cure or non-treatment diseases and only some of their symptoms can be beaten. Understanding how the diseases behave implies a complex analysis that together with the new technologies provide researchers with more calculation and observational capabilities, as well as novel approaches that allow us to observe how the diseases behave and relate in different environments with distinct factors. Current research aims to find new ways of characterizing the diseases based on phenotypic manifestations using knowledge extraction techniques from public sources. With the characterization of the diseases, a better understanding about the diseases and how similar they are can be achieved, leading for example to find new drugs that can be applied to different diseases. In order to carry out the present research we have made use of our own dataset of symptoms and diseases developed using an approach that allows us to generate phenotypic knowledge from the extraction of medical information from several data sources.","PeriodicalId":74567,"journal":{"name":"Proceedings. IEEE International Symposium on Computer-Based Medical Systems","volume":"16 1","pages":"596-599"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Characterization of Diseases Based on Phenotypic Information Through Knowledge Extraction using Public Sources\",\"authors\":\"Gerardo Lagunes García, A. R. González\",\"doi\":\"10.1109/CBMS.2019.00124\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Despite the huge findings made by the study of the behaviour of diseases, there are currently many non-cure or non-treatment diseases and only some of their symptoms can be beaten. Understanding how the diseases behave implies a complex analysis that together with the new technologies provide researchers with more calculation and observational capabilities, as well as novel approaches that allow us to observe how the diseases behave and relate in different environments with distinct factors. Current research aims to find new ways of characterizing the diseases based on phenotypic manifestations using knowledge extraction techniques from public sources. With the characterization of the diseases, a better understanding about the diseases and how similar they are can be achieved, leading for example to find new drugs that can be applied to different diseases. In order to carry out the present research we have made use of our own dataset of symptoms and diseases developed using an approach that allows us to generate phenotypic knowledge from the extraction of medical information from several data sources.\",\"PeriodicalId\":74567,\"journal\":{\"name\":\"Proceedings. IEEE International Symposium on Computer-Based Medical Systems\",\"volume\":\"16 1\",\"pages\":\"596-599\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-06-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings. IEEE International Symposium on Computer-Based Medical Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CBMS.2019.00124\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. IEEE International Symposium on Computer-Based Medical Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CBMS.2019.00124","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

尽管对疾病行为的研究取得了巨大的发现,但目前仍有许多无法治愈或无法治疗的疾病,只有一些症状是可以战胜的。了解这些疾病的行为方式意味着需要进行复杂的分析,这种分析与新技术一起为研究人员提供了更多的计算和观察能力,以及新的方法,使我们能够观察疾病的行为方式以及在不同环境中与不同因素的关系。目前的研究旨在利用公共资源的知识提取技术,寻找基于表型表现的疾病特征的新方法。有了这些疾病的特征,就可以更好地了解这些疾病及其相似程度,例如,可以找到适用于不同疾病的新药。为了开展目前的研究,我们使用了我们自己的症状和疾病数据集,使用一种方法,使我们能够从几个数据源中提取医学信息,从而产生表型知识。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Characterization of Diseases Based on Phenotypic Information Through Knowledge Extraction using Public Sources
Despite the huge findings made by the study of the behaviour of diseases, there are currently many non-cure or non-treatment diseases and only some of their symptoms can be beaten. Understanding how the diseases behave implies a complex analysis that together with the new technologies provide researchers with more calculation and observational capabilities, as well as novel approaches that allow us to observe how the diseases behave and relate in different environments with distinct factors. Current research aims to find new ways of characterizing the diseases based on phenotypic manifestations using knowledge extraction techniques from public sources. With the characterization of the diseases, a better understanding about the diseases and how similar they are can be achieved, leading for example to find new drugs that can be applied to different diseases. In order to carry out the present research we have made use of our own dataset of symptoms and diseases developed using an approach that allows us to generate phenotypic knowledge from the extraction of medical information from several data sources.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Automated Design of Task-Dedicated Illumination with Particle Swarm Optimization Automatic Polyp Segmentation with Multiple Kernel Dilated Convolution Network. Video Capsule Endoscopy Classification using Focal Modulation Guided Convolutional Neural Network. A Gamification-Based Framework for mHealth Developers in the Context of Self-Care Mental Health Ubiquitous Monitoring: Detecting Context-Enriched Sociability Patterns Through Complex Event Processing
×
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
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1