ArticleRevista brasileira de epidemiologia = Brazilian journal of epidemiology2023
Emergency department use and Artificial Intelligence in Pelotas: design and baseline results.
Article in Revista brasileira de epidemiologia = Brazilian journal of epidemiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Using artificial intelligence to analyze violence against women in São Paulo.Revista de saude publica · 2026Article
- Artificial intelligence in Brazilian public health: potential, challenges, and ethical implications for the Brazilian Unified Health System.Revista brasileira de epidemiologia = Brazilian journal of epidemiology · 2026Article
- Adult use of health services: A cross-sectional population-based study, Pelotas, Brazil, 2012 and 2021.Epidemiologia e servicos de saude : revista do Sistema Unico de Saude do Brasil · 2026Article
- Prediction of atelectasis inFrontiers in pediatrics · 2026Article
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Authors and funding
21 authors.
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Abstract
objetivoTo 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.
methodsThe 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.
resultsIn 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.
conclusionThe 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.
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