• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

疟疾可视化分析工具：用于对巴西公共疟疾数据进行可视化探索性分析的工具。

Malaria-VisAnalytics: a tool for visual exploratory analysis of Brazilian public malaria data.

机构信息

AtyImoLab - Institute of Computing, Federal University of Bahia (UFBA), Salvador, Brazil.

Institute of Collective Health, Federal University of Bahia (UFBA), Salvador, Brazil.

出版信息

Malar J. 2022 Aug 1;21(1):232. doi: 10.1186/s12936-022-04248-w.

DOI:10.1186/s12936-022-04248-w
PMID:35915484
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9344676/
Abstract

BACKGROUND

Data integration and visualisation techniques have been widely used in scientific research to allow the exploitation of large volumes of data and support highly complex or long-lasting research questions. Integration allows data from different sources to be aggregated into a single database comprising variables of interest for different types of studies. Visualisation allows large and complex data sets to be manipulated and interpreted in a more intuitive way.

METHODS

Integration and visualisation techniques were applied in a malaria surveillance ecosystem to build an integrated database comprising notifications, deaths, vector control and climate data. This database is accessed through Malaria-VisAnalytics, a visual mining platform for descriptive and predictive analysis supporting decision and policy-making by governmental and health agents.

RESULTS

Experimental and validation results have proved that the visual exploration and interaction mechanisms allow effective surveillance for rapid action in suspected outbreaks, as well as support a set of different research questions over integrated malaria electronic health records.

CONCLUSION

The integrated database and the visual mining platform (Malaria-VisAnalytics) allow different types of users to explore malaria-related data in a user-friendly interface. Summary data and key insights can be obtained through different techniques and dimensions. The case study on Manaus can serve as a reference for future replication in other municipalities. Finally, both the database and the visual mining platform can be extended with new data sources and functionalities to accommodate more complex scenarios (such as real-time data capture and analysis).

摘要

背景

数据集成和可视化技术已广泛应用于科学研究中，以利用大量数据并支持高度复杂或耗时长久的研究问题。集成可将来自不同来源的数据聚合到一个包含不同类型研究感兴趣变量的单个数据库中。可视化可更直观地操作和解释大型和复杂数据集。

方法

集成和可视化技术应用于疟疾监测生态系统中，构建了一个包含通知、死亡、病媒控制和气候数据的综合数据库。该数据库通过 Malaria-VisAnalytics 访问，这是一个用于描述性和预测性分析的可视化挖掘平台，为政府和卫生机构的决策和政策制定提供支持。

结果

实验和验证结果证明，可视化探索和交互机制允许对疑似暴发进行快速行动的有效监测，并支持对综合疟疾电子健康记录进行一系列不同的研究问题。

结论

综合数据库和可视化挖掘平台（Malaria-VisAnalytics）允许不同类型的用户在用户友好的界面中探索与疟疾相关的数据。通过不同的技术和维度可以获得汇总数据和关键见解。马瑙斯的案例研究可以作为未来在其他城市复制的参考。最后，数据库和可视化挖掘平台都可以通过新的数据源和功能进行扩展，以适应更复杂的场景（例如实时数据捕获和分析）。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/346f/9344676/8ab8200e20ee/12936_2022_4248_Fig17_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/346f/9344676/663e299fe28a/12936_2022_4248_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/346f/9344676/d690002601c4/12936_2022_4248_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/346f/9344676/d9c8659ab065/12936_2022_4248_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/346f/9344676/47d12ed7c315/12936_2022_4248_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/346f/9344676/585b76fbd0d7/12936_2022_4248_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/346f/9344676/76376b5ec55d/12936_2022_4248_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/346f/9344676/e3995bbc25c7/12936_2022_4248_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/346f/9344676/2baa97547174/12936_2022_4248_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/346f/9344676/303ea7bb78d4/12936_2022_4248_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/346f/9344676/bd0140f504ce/12936_2022_4248_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/346f/9344676/964a855b27ba/12936_2022_4248_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/346f/9344676/5b2ee5c88825/12936_2022_4248_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/346f/9344676/5c23e370e07e/12936_2022_4248_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/346f/9344676/a83f50494f01/12936_2022_4248_Fig14_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/346f/9344676/9e03c863068a/12936_2022_4248_Fig15_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/346f/9344676/cdd961766d9c/12936_2022_4248_Fig16_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/346f/9344676/8ab8200e20ee/12936_2022_4248_Fig17_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/346f/9344676/663e299fe28a/12936_2022_4248_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/346f/9344676/d690002601c4/12936_2022_4248_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/346f/9344676/d9c8659ab065/12936_2022_4248_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/346f/9344676/47d12ed7c315/12936_2022_4248_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/346f/9344676/585b76fbd0d7/12936_2022_4248_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/346f/9344676/76376b5ec55d/12936_2022_4248_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/346f/9344676/e3995bbc25c7/12936_2022_4248_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/346f/9344676/2baa97547174/12936_2022_4248_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/346f/9344676/303ea7bb78d4/12936_2022_4248_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/346f/9344676/bd0140f504ce/12936_2022_4248_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/346f/9344676/964a855b27ba/12936_2022_4248_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/346f/9344676/5b2ee5c88825/12936_2022_4248_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/346f/9344676/5c23e370e07e/12936_2022_4248_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/346f/9344676/a83f50494f01/12936_2022_4248_Fig14_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/346f/9344676/9e03c863068a/12936_2022_4248_Fig15_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/346f/9344676/cdd961766d9c/12936_2022_4248_Fig16_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/346f/9344676/8ab8200e20ee/12936_2022_4248_Fig17_HTML.jpg

相似文献

1
Malaria-VisAnalytics: a tool for visual exploratory analysis of Brazilian public malaria data.疟疾可视化分析工具：用于对巴西公共疟疾数据进行可视化探索性分析的工具。
Malar J. 2022 Aug 1;21(1):232. doi: 10.1186/s12936-022-04248-w.
2
Spatial dependence of malaria epidemics in municipalities of the Amazonian Ecosystem.亚马逊生态系统各城市疟疾流行的空间依赖性。
Rev Bras Epidemiol. 2014 Jul-Sep;17(3):615-28. doi: 10.1590/1809-4503201400030004.
3
Data visualisation in surveillance for injury prevention and control: conceptual bases and case studies.伤害预防与控制监测中的数据可视化：概念基础与案例研究
Inj Prev. 2016 Apr;22 Suppl 1(Suppl 1):i27-33. doi: 10.1136/injuryprev-2015-041812. Epub 2016 Jan 4.
4
Development of traditional Chinese medicine clinical data warehouse for medical knowledge discovery and decision support.中医药临床数据仓库的开发用于医学知识发现和决策支持。
Artif Intell Med. 2010 Feb-Mar;48(2-3):139-52. doi: 10.1016/j.artmed.2009.07.012. Epub 2010 Feb 1.
5
An integrated dataset of malaria notifications in the Legal Amazon.亚马孙法定地区疟疾通报的综合数据集。
BMC Res Notes. 2020 Jun 3;13(1):274. doi: 10.1186/s13104-020-05109-y.
6
Reaching the malaria elimination goal in Brazil: a spatial analysis and time-series study.在巴西实现消除疟疾目标：空间分析和时间序列研究。
Infect Dis Poverty. 2022 Apr 5;11(1):39. doi: 10.1186/s40249-022-00945-5.
7
Uncomplicated malaria among pregnant women in the Brazilian Amazon: local barriers to prompt and effective case management.巴西亚马逊地区孕妇中非复杂性疟疾：迅速有效病例管理的当地障碍。
Acta Trop. 2013 Feb;125(2):137-42. doi: 10.1016/j.actatropica.2012.11.004. Epub 2012 Nov 21.
8
Spatial distribution of malaria and primary healthcare in Cametá and Tucuruí, Pará state, Brazil.巴西帕拉州卡梅塔和图卡雷里的疟疾和初级卫生保健的空间分布。
J Infect Dev Ctries. 2022 Jan 31;16(1):206-212. doi: 10.3855/jidc.15260.
9
Cost-effectiveness of diagnostic for malaria in Extra-Amazon Region, Brazil.巴西亚马孙河以外地区疟疾诊断的成本效益分析。
Malar J. 2012 Nov 23;11:390. doi: 10.1186/1475-2875-11-390.
10
Easier surveillance of climate-related health vulnerabilities through a Web-based spatial OLAP application.通过基于网络的空间联机分析处理应用程序，更轻松地监测与气候相关的健康脆弱性。
Int J Health Geogr. 2009 Apr 3;8:18. doi: 10.1186/1476-072X-8-18.

引用本文的文献

1
Analysis of inpatient data on dengue fever, malaria and leishmaniasis in Ecuador: A cross-sectional national study, 2015-2022.厄瓜多尔登革热、疟疾和利什曼病住院患者数据的分析：一项2015 - 2022年的全国性横断面研究。
New Microbes New Infect. 2024 May 11;60-61:101421. doi: 10.1016/j.nmni.2024.101421. eCollection 2024 Aug-Oct.

本文引用的文献

1
The association between a conditional cash transfer programme and malaria incidence: a longitudinal ecological study in the Brazilian Amazon between 2004 and 2015.有条件现金转移支付计划与疟疾发病率之间的关联：2004年至2015年巴西亚马逊地区的一项纵向生态研究。
BMC Public Health. 2021 Jun 29;21(1):1253. doi: 10.1186/s12889-021-11255-0.
2
Effect of a conditional cash transfer programme on childhood mortality: a nationwide analysis of Brazilian municipalities.有条件现金转移计划对儿童死亡率的影响：巴西全国各城市的分析。
Lancet. 2013 Jul 6;382(9886):57-64. doi: 10.1016/S0140-6736(13)60715-1. Epub 2013 May 15.
3
The malaria Atlas Project: developing global maps of malaria risk.
疟疾地图计划：绘制全球疟疾风险地图
PLoS Med. 2006 Dec;3(12):e473. doi: 10.1371/journal.pmed.0030473.
4
Spatio-temporal distribution of Plasmodium falciparum and p. Vivax malaria in Thailand.泰国恶性疟原虫和间日疟原虫疟疾的时空分布
Am J Trop Med Hyg. 2005 Mar;72(3):256-62.
5
Enhancing scatterplots with smoothed densities.用平滑密度增强散点图。
Bioinformatics. 2004 Mar 22;20(5):623-8. doi: 10.1093/bioinformatics/btg454. Epub 2004 Jan 22.
6
The U.S. EPA Geographic Information System for mapping environmental releases of Toxic Chemical Release Inventory (TRI) chemicals.美国环境保护局用于绘制有毒化学品排放清单（TRI）化学品环境排放情况的地理信息系统。
Risk Anal. 1993 Apr;13(2):155-64. doi: 10.1111/j.1539-6924.1993.tb01065.x.