使用数据挖掘技术检测药物不良反应:综述文章

Behnaz Pourebrahim, M. Keyvanpour
{"title":"使用数据挖掘技术检测药物不良反应:综述文章","authors":"Behnaz Pourebrahim, M. Keyvanpour","doi":"10.1109/ICCKE50421.2020.9303709","DOIUrl":null,"url":null,"abstract":"Adverse drug reactions (ADRs) are side effects that occur when taking the drug in natural doses. ADRs are a public health issue because they hospitalize millions of patients worldwide each year. Early detection of ADRs reduces economic costs and prevents fatality.Diagnosis of ADRs usually depended on voluntary reporting or medical information. But in recent years, the data sent by the user on social media has become a significant source for detecting ADR. Twitter is a social media where people use short messages as a way to communicate. Limit the number of words on Twitter allows users to use words purposefully and focused. The information provided by users about drugs and their adverse reactions on Twitter is an important resource for post-marketing drug monitoring.In recent years, machine learning and data mining methods have been considered in the field of data science for ADR detection. Important challenges in this area are divided into three parts: data pre-processing, extracting meaningful features, and selecting the best model for classification.The aim of this study is to study, review and challenge the methods of ADR diagnosis by data mining on social media, especially Twitter.","PeriodicalId":402043,"journal":{"name":"2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Adverse Drug Reaction Detection Using Data Mining Techniques: A Review Article\",\"authors\":\"Behnaz Pourebrahim, M. Keyvanpour\",\"doi\":\"10.1109/ICCKE50421.2020.9303709\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Adverse drug reactions (ADRs) are side effects that occur when taking the drug in natural doses. ADRs are a public health issue because they hospitalize millions of patients worldwide each year. Early detection of ADRs reduces economic costs and prevents fatality.Diagnosis of ADRs usually depended on voluntary reporting or medical information. But in recent years, the data sent by the user on social media has become a significant source for detecting ADR. Twitter is a social media where people use short messages as a way to communicate. Limit the number of words on Twitter allows users to use words purposefully and focused. The information provided by users about drugs and their adverse reactions on Twitter is an important resource for post-marketing drug monitoring.In recent years, machine learning and data mining methods have been considered in the field of data science for ADR detection. Important challenges in this area are divided into three parts: data pre-processing, extracting meaningful features, and selecting the best model for classification.The aim of this study is to study, review and challenge the methods of ADR diagnosis by data mining on social media, especially Twitter.\",\"PeriodicalId\":402043,\"journal\":{\"name\":\"2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)\",\"volume\":\"80 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCKE50421.2020.9303709\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCKE50421.2020.9303709","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

摘要

药物不良反应(adr)是指以自然剂量服用药物时发生的副作用。不良反应是一个公共卫生问题,因为全世界每年有数百万患者因此住院。早期发现不良反应可降低经济成本并防止死亡。不良反应的诊断通常依赖于自愿报告或医疗信息。但近年来,用户在社交媒体上发送的数据已经成为检测ADR的重要来源。推特是一种社交媒体,人们使用短信作为交流的方式。限制推特上的字数可以让用户有目的地和专注地使用单词。用户在Twitter上提供的有关药品及其不良反应的信息是药品上市后监测的重要资源。近年来,机器学习和数据挖掘方法被认为是ADR检测的数据科学领域。该领域的重要挑战分为三个部分:数据预处理、提取有意义的特征和选择最佳模型进行分类。本研究的目的是通过社交媒体,特别是Twitter上的数据挖掘来研究、回顾和挑战ADR诊断方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adverse Drug Reaction Detection Using Data Mining Techniques: A Review Article
Adverse drug reactions (ADRs) are side effects that occur when taking the drug in natural doses. ADRs are a public health issue because they hospitalize millions of patients worldwide each year. Early detection of ADRs reduces economic costs and prevents fatality.Diagnosis of ADRs usually depended on voluntary reporting or medical information. But in recent years, the data sent by the user on social media has become a significant source for detecting ADR. Twitter is a social media where people use short messages as a way to communicate. Limit the number of words on Twitter allows users to use words purposefully and focused. The information provided by users about drugs and their adverse reactions on Twitter is an important resource for post-marketing drug monitoring.In recent years, machine learning and data mining methods have been considered in the field of data science for ADR detection. Important challenges in this area are divided into three parts: data pre-processing, extracting meaningful features, and selecting the best model for classification.The aim of this study is to study, review and challenge the methods of ADR diagnosis by data mining on social media, especially Twitter.
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
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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