Banca de DEFESA: RODRIGO DANTAS DA SILVA

Uma banca de DEFESA de MESTRADO foi cadastrada pelo programa.
STUDENT : RODRIGO DANTAS DA SILVA
DATE: 14/08/2020
TIME: 08:00
LOCAL: Defesa por webconferência
TITLE:

DATA DRIVEN PREDICTIVE ANALYTICS TO PROFILING RISK GROUPS IN SUS: A CASE STUDY APPLIED TO SYPHILIS IN BRAZIL

KEY WORDS:

BIG DATA, DATA DRIVEN, HEALTHCARE

PAGES: 32
BIG AREA: Ciências da Saúde
AREA: Medicina
SUMMARY:

For many decades the society has had the need to monitor and assess the standard of living of the population. In the 1950s, the United Nations (UN) saw this need and proposed 12 areas that should be evaluated, being the first one of the areas listed as “Health and Demography”, focused on what is expressed as the health level of a population. Decades has passed and great results have been gained from similar initiatives such as reducing mortality by infectious diseases and even eradicating some others. In the age of the digital society, needs have grown and horizons have widened. Monitoring demands that once perished from data to become concrete now suffer from the opposite effect, the excess of data from everywhere. Healthcare systems around the world use many different information systems, collecting and generating hundreds of data at unimaginable speed. We are billions of people on the planet and most of us are connected to the virtual world, sharing information, experiences and events with the cloud. In this information age, the ability to aggregate and process this data is a major factor in raising public health to a new level. The development of tools capable of analyzing a large volume of data in seconds and producing knowledge for targeted decision making can help in the fight against specific diseases, in the process of continuing education of professionals, in the formation of new professionals, in the elaboration of new policies with the specific locoregional look, in the analysis of hidden trends in front of so much information faced in everyday life and other possibilities. The present work proposes an architecture capable of handling this large amount of data and a systematic capable of producing, from this data, predictive analyzes for risk groups.

BANKING MEMBERS:
Externo à Instituição - AGNALDO SOUZA CRUZ
Externo ao Programa - 2459504 - AQUILES MEDEIROS FILGUEIRA BURLAMAQUI
Externo à Instituição - CARLOS ALBERTO PEREIRA DE OLIVEIRA - UNEMAT
Interna - 2562782 - KARILANY DANTAS COUTINHO
Externo à Instituição - LEONARDO JUDSON GALVAO DE LIMA
Presidente - 2488270 - RICARDO ALEXSANDRO DE MEDEIROS VALENTIM
Externa à Instituição - THAISA GOIS FARIAS DE MOURA SANTOS LIMA - MS