Rafael Tavares Rufino
ABSTRACT
The numbers of dengue fever cases in Brazil have grown in recent years, with the arrival of other diseases related to the mosquito that transmits dengue fever (Aedes Aegypt), the situation is very alarming. This paper presents the development of Dengue 360, a tool built on the concepts and processes of business intelligence, which can be used to assist the analysis and dissemination of the epidemiological situation in a region. The goal is to provide information through maps, graphs and other visual artifacts that serve as a basis for decision-making by health managers so that they can create more effective prevention and control policies, as well as facilitate access to information about dengue fever in the region in which they live.
- N. H. Z. Abai, J. H. Yahaya, and A. Deraman. 2015. Incorporating business intelligence and analytics into performance management for the public sector issues and challenges. In 2015 International Conference on Electrical Engineering and Informatics (ICEEI). 484--489.Google Scholar
- Z. A. Al-Sai and L. M. Abualigah. 2017. Big data and E-government: A review. In 2017 8th International Conference on Information Technology (ICIT). 580--587.Google Scholar
- O. Ali, P. Crvenkovski, and H.Johnson. 2016. Using a business intelligence data analytics solution in healthcare. In 2016 IEEE 7th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). 1--6.Google Scholar
- K. Böhm, A. Mehler-Bicher, and D. Fenchel. 2011. GeoVisualAnalytics in the public health sector. In Proceedings 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services. 291--294.Google Scholar
- S. Chaudhuri, U. Dayal, and V. Narasayya. {n. d.}. An Overview of Business Intelligence Technology. Commun. ACM 54, 8 ({n. d.}). Google ScholarDigital Library
- Secretaria de vigilância em Saúde. 2016. Monitoramento dos casos de dengue, febre Chikungunya e febre pelo vírus Zika até a semana epidemiológica 49. Ministério da Saúde, Brasília, DF.Google Scholar
- E. Gamma, R. Helm, R.Johnson, and J. Vlissides. 1994. Design Patterns: Elements of Reusable Object-Oriented Software. Addison-Wesley, Indianapolis, IN. Google ScholarDigital Library
- P. Gonzalez-Alonso, R. Vilar, and F. Lupiañez-Villanueva. 2017. Meeting Technology and Methodology into Health Big Data Analytics Scenarios. In 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS). 284--285.Google Scholar
- W. H. Inmon. 2002. Building the Data Warehouse (4 ed.). John Wiley and Sons, New York, NY. Google ScholarDigital Library
- K. Jee and G. H. Kim. 2013. Potentiality of Big Data in the Medical Sector: Focus on How to Reshape the Healthcare System. Healthcare Informatics Research 19, 2 (jun 2013), 79--85.Google ScholarCross Ref
- I. Kigwana, V. R. Kebande, and H. S. Venter. 2017. Towards an e-government framework for the Republic of Uganda. In 2017 IST-Africa Week Conference (IST-Africa). 1--9.Google Scholar
- R. Kimball and M. Ross. 2002. The Data Warehouse Toolkit. John Wiley and Sons, New York, NY. Google ScholarDigital Library
- P. Loreto, F. Fonseca, A. Morais, H. Peixoto, A. Abelha, and J. Machado. 2017. Improving Maternity Care with Business Intelligence. In 2017 5th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW). 170--177.Google Scholar
- Pan American Health Organization and World Health Organization. 2016. Epidemiological Update: Neurological syndrome, congenital anomalies and Zika virus infection. PAHO and WHO, Washington, DC.Google Scholar
- P. Shrivastava and M. Shukla. 2014. A Study on Some of Data Warehouses and Data Mining (Case Study of Data Mining for Environmental Problems). International Journal of Computer Science Trends and Technology 2, 1 (jan-feb 2014), 36--43.Google Scholar
- J. Su and Y. Tang. 2017. Business Intelligence Revisited. In 2017 Fifth International Conference on Advanced Cloud and Big Data. 1--6.Google Scholar
- K. Sunassee, T. Vythilingum, and R. K. Sungkur. 2017. Providing improved services to citizens, a critical review of E-government facilities. In 2017 1st International Conference on Next Generation Computing Applications (NextComp). 129--134.Google Scholar
- G. S. Fajar Surya and A. Amalia. 2017. The critical success factors model for e-Government implementation in Indonesia. In 2017 5th International Conference on Information and Communication Technology (ICoIC7). 1--5.Google Scholar
- E. Turban and L. Volonimo. 2013. Business Intelligence e Suporte á Decisão. In Tecnologia da Informaćão para Gestão: em busca do melhor desempenho estratégico e operacional (8 ed.). Bookman, Porto Alegre, RS.Google Scholar
Index Terms
- Dengue 360: a business intelligence tool for analysis and dissemination of epidemiological situation
Recommendations
Using Electronic Health Records and Data Warehouse Collaboratively in Community Health Centers
The organization Community Partners HealthNet CPH, Inc. is a so-called Health-Center-Controlled Networks HCCNs that provide health information technologies, in particular Electronic Health Records and Data Warehouse, to participating community health ...
Business Analytics Components for Public Health Institution - Clinical Decision Area
AbstractThe practice of evidence-based medicine has been gaining ground in the most diverse health institutions and consequently in the work of their health professionals. Thus, the Clinical Decision Support Systems appear, which aim to ensure that health ...
The Diagnosis of Dengue Disease: An Evaluation of Three Machine Learning Approaches
This article describes how Dengue fever is a fatal and hazardous disease resulting from the bite of several species of the female mosquito principally, Aedesaegypti. Symptoms of the dengue fever mimic those of a number of other infectious and/or ...
Comments