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

• Published in: Revista brasileira de epidemiologia Vol. 26; p. e230021
• Main Authors: Delpino, Felipe Mendes, Figueiredo, Lílian Munhoz, Costa, Ândria Krolow, Carreno, Ioná, Silva, Luan Nascimento da, Flores, Alana Duarte, Pinheiro, Milena Afonso, Silva, Eloisa Porciúncula da, Marques, Gabriela Ávila, Saes, Mirelle de Oliveira, Duro, Suele Manjourany Silva, Facchini, Luiz Augusto, Vissoci, João Ricardo Nickenig, Flores, Thaynã Ramos, Demarco, Flávio Fernando, Blumenberg, Cauane, Chiavegatto Filho, Alexandre Dias Porto, Silva, Inácio Crochemore da, Batista, Sandro Rodrigues, Arcêncio, Ricardo Alexandre, Nunes, Bruno Pereira
• Format: Journal Article
• Language: English
• Published: Brazil Associação Brasileira de Saúde Coletiva 2023; Associação Brasileira de Pós -Graduação em Saúde Coletiva; Associação Brasileira de Pós-Graduação em Saúde Coletiva
• Subjects: Adult; Artificial Intelligence; Artigo Original; Brazil; Chronic diseases; Emergency Service, Hospital; Female; Health Policy & Services; Humans; Machine learning; Male; Middle Aged; Multimorbidity; Obesity; Socioeconomic Factors; Urgent and emergency care; Brazil; Machine learning; Doenças crônicas; Chronic diseases; Aprendizado de máquina; Urgent and emergency care; Multimorbidade; Urgência e emergência; Multimorbidity
• ISSN: 1415-790X; 1980-5497
• DOI: 10.1590/1980-549720230021

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. 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. 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. 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.