Banca de DEFESA: TIAGO DE OLIVEIRA BARRETO

Uma banca de DEFESA de DOUTORADO foi cadastrada pelo programa.
STUDENT : TIAGO DE OLIVEIRA BARRETO
DATE: 27/09/2024
TIME: 09:00
LOCAL: Laboratório de Inovação Tecnológica em Saúde (LAIS)
TITLE:

ARTIFICIAL INTELLIGENCE APPLIED TO THE REGULATION ECOSYSTEM OF THE STATE OF RIO GRANDE DO NORTE (REGULA RN): ANALYZES BASED ON MACHINE LEARNING IN COVID-19 BEDS AND GENERAL BEDS

KEY WORDS:

Bed Regulation, RegulaRN, Artificial Intelligence, Computational Models, Digital Health

PAGES: 68
BIG AREA: Engenharias
AREA: Engenharia Biomédica
SUMMARY:

The process of regulating beds is among the most relevant processes for the Brazilian public health system. It encompasses the entire process of managing and monitoring a patient who requires hospitalization, from the request to their proper admission. However, it is still an area that has little investment in digital health systems and other resources that can favor the better management of the regulatory process. Thus, this work aims to include the area of artificial intelligence within the area of regulating public beds, in order to enhance and assist the decision-making process during bed regulation. In this sense, bed regulation data from two modules of the platform adopted in Rio Grande do Norte, RegulaRN COVID-19 and RegulaRN Leitos Gerais, were used. In total, approximately 72,422 bed regulation data were analyzed in different time frames. In addition, a pipeline of characterization, preprocessing, data correlation, definition of metrics for evaluation, data balancing, definition of training and validation data, definition of computational models for data classification and selection of hyperparameters was used. For the RegulaRN COVID-19 platform, the results showed better performance for the accuracy (84.01%), precision (79.57%) and F1-score (81.00%) metrics in the Multilayer Perceptron model with Stochastic Gradient Descent (SGD) optimizer. For the recall (84.67%), specificity (84.67%) and ROC-AUC (91.6%) metrics, the best results were obtained by RMSProp. Regarding the data from RegulaRN Leitos Gerais, XGBoost presented the best accuracy (87.77%) and recall (87.77%) values, Random Forest had the best precision (87.05%), Gradient Boosting had the best F1 Score (87.56%) and for specificity (82.94%) it was obtained by SGD. The results allowed us to identify the best models to assist healthcare professionals during the bed regulation process, as well as the scientific findings of this academic work demonstrate that the computational methods used applied through a digital health solution can assist in the decision-making of medical regulators and government institutions in order to strengthen the performance of Brazilian public health.

COMMITTEE MEMBERS:
Presidente - 2488270 - RICARDO ALEXSANDRO DE MEDEIROS VALENTIM
Interno - 347628 - ADRIAO DUARTE DORIA NETO
Externa ao Programa - 8277481 - LYANE RAMALHO CORTEZ - UFRNExterno à Instituição - ANTONIO HIGOR FREIRE DE MORAIS - IFRN
Externo à Instituição - GUILHERME MEDEIROS MACHADO
Externo à Instituição - JOÃO PAULO QUEIROZ DOS SANTOS - IFRN