Emergency department use and Artificial Intelligence in Pelotas: design and baseline results

Revista Brasileira de Epidemiologia

article

Delpino, Felipe Mendes¹  Figueiredo, Lílian Munhoz¹  Costa, Ândria Krolow¹  Carreno, Ioná¹  Silva, Luan Nascimento da¹  Flores, Alana Duarte¹  Pinheiro, Milena Afonso¹  Silva, Eloisa Porciúncula da¹  Marques, Gabriela Ávila¹  Saes, Mirelle de Oliveira¹  Duro, Suele Manjourany Silva¹  Facchini, Luiz Augusto¹  Vissoci, João Ricardo Nickenig²  Flores, Thaynã Ramos¹  Demarco, Flávio Fernando¹  Blumenberg, Cauane¹  Chiavegatto Filho, Alexandre Dias Porto³  Silva, Inácio Crochemore da¹  Batista, Sandro Rodrigues⁴  Arcêncio, Ricardo Alexandre³  Nunes, Bruno Pereira¹ 

[1] Universidade Federal de Pelotas;Duke University School of Medicine;Universidade de São Paulo;Universidade Federal de Goias

关键词: Machine learning;    Chronic diseases;    Multimorbidity;    Urgent and emergency care;   

DOI  :  10.1590/1980-549720230021

学科分类：社会科学、人文和艺术（综合）

来源: SciELO

【 摘 要 】

Objetivo: To describe the initial baseline results of a population-based study, as well as a protocol in order to evaluate the performance of different machine learning algorithms with the objective of predicting the demand for urgent and emergency services in a representative sample of adults from the urban area of Pelotas, Southern Brazil.Methods: The study is entitled “Emergency department use and Artificial Intelligence in PELOTAS (RS) (EAI PELOTAS)” (https://wp.ufpel.edu.br/eaipelotas/). Between September and December 2021, a baseline was carried out with participants. A follow-up was planned to be conducted after 12 months in order to assess the use of urgent and emergency services in the last year. Afterwards, machine learning algorithms will be tested to predict the use of urgent and emergency services over one year.Results: In total, 5,722 participants answered the survey, mostly females (66.8%), with an average age of 50.3 years. The mean number of household people was 2.6. Most of the sample has white skin color and incomplete elementary school or less. Around 30% of the sample has obesity, 14% diabetes, and 39% hypertension.Conclusion: The present paper presented a protocol describing the steps that were and will be taken to produce a model capable of predicting the demand for urgent and emergency services in one year among residents of Pelotas, in Rio Grande do Sul state.

【 授权许可】

CC BY-NC