EVOTION – Big Data Supporting Public Hearing Health Policies

J. Christensen, N. H. Pontoppidan
{"title":"EVOTION – Big Data Supporting Public Hearing Health Policies","authors":"J. Christensen, N. H. Pontoppidan","doi":"10.1109/CBMS.2019.00012","DOIUrl":null,"url":null,"abstract":"Hearing Loss (HL) is a highly prevalent chronic disease (the 5th cause of disability world-wide), which increases the risk of cognitive decline, mental illness, and depression, and furthermore leads to social isolation, unemployment/early retirement, loss of income and work discrimination. To enable successful holistic management of HL, appropriate public health policies for HL prevention, early diagnosis, long-term treatment and rehabilitation are required. In addition, HL management would benefit from detection and prevention of cognitive decline; protection from noise; and initiatives for socioeconomic inclusion of HL patients. However, the evidence for forming such policies and enabling true holistic HL management is lacking. Specifically, holistic HL management policies require access to and analysis of heterogeneous data sources. In EVOTION, such big data from five different clinical organizations are available and continuous acquisition of real-time data produced by sensors and hearing aids used by HL patients will support their continuous update. In order to utilize these data in forming holistic HL management policies, EVOTION is developing an integrated IT platform supporting: 1) the acquisition and analysis of heterogeneous big data related to HL; 2) policy decision making, i.e. selection of effective interventions related to the holistic management of HL based on the outcomes of 1) and the formulation of related public health policies; and 3) specification and continuous monitoring of the effects of such policies in a sustainable manner.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CBMS.2019.00012","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

Abstract

Hearing Loss (HL) is a highly prevalent chronic disease (the 5th cause of disability world-wide), which increases the risk of cognitive decline, mental illness, and depression, and furthermore leads to social isolation, unemployment/early retirement, loss of income and work discrimination. To enable successful holistic management of HL, appropriate public health policies for HL prevention, early diagnosis, long-term treatment and rehabilitation are required. In addition, HL management would benefit from detection and prevention of cognitive decline; protection from noise; and initiatives for socioeconomic inclusion of HL patients. However, the evidence for forming such policies and enabling true holistic HL management is lacking. Specifically, holistic HL management policies require access to and analysis of heterogeneous data sources. In EVOTION, such big data from five different clinical organizations are available and continuous acquisition of real-time data produced by sensors and hearing aids used by HL patients will support their continuous update. In order to utilize these data in forming holistic HL management policies, EVOTION is developing an integrated IT platform supporting: 1) the acquisition and analysis of heterogeneous big data related to HL; 2) policy decision making, i.e. selection of effective interventions related to the holistic management of HL based on the outcomes of 1) and the formulation of related public health policies; and 3) specification and continuous monitoring of the effects of such policies in a sustainable manner.
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
进化-支持公共听力健康政策的大数据
听力损失是一种非常普遍的慢性疾病(全球第五大致残原因),它增加了认知能力下降、精神疾病和抑郁症的风险,并进一步导致社会孤立、失业/提前退休、收入损失和工作歧视。为了成功地全面管理HL,需要制定适当的HL预防、早期诊断、长期治疗和康复公共卫生政策。此外,发现和预防认知能力下降将有利于HL的管理;防止噪音;以及促进HL患者社会经济包容的举措。然而,形成这样的政策和实现真正的整体HL管理的证据是缺乏的。具体来说,整体HL管理策略需要访问和分析异构数据源。在EVOTION中,来自五个不同临床机构的大数据是可用的,HL患者使用的传感器和助听器产生的实时数据的持续采集将支持它们的持续更新。为了利用这些数据形成整体的HL管理政策,evoltion正在开发一个集成的IT平台,支持:1)获取和分析HL相关的异构大数据;2)政策决策,即根据1)的结果选择与HL整体管理相关的有效干预措施和制定相关公共卫生政策;3)以可持续的方式规范和持续监测这些政策的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Analysing the Performance of a Real-Time Healthcare 4.0 System using Shared Frailty Time to Event Models Performance of Data Enhancements and Training Optimization for Neural Network: A Polyp Detection Case Study I Know How you Feel Now, and Here's why!: Demystifying Time-Continuous High Resolution Text-Based Affect Predictions in the Wild Identifying Diabetic Retinopathy from OCT Images using Deep Transfer Learning with Artificial Neural Networks Towards an Analysis of Post-Transcriptional Gene Regulation in Psoriasis via microRNAs using Machine Learning Algorithms
×
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