Universidade Federal de Pelotas - Pelotas (RS), Brazil.
Duke University School of Medicine - Durham (NC), United States.
Rev Bras Epidemiol. 2023 Mar 10;26:e230021. doi: 10.1590/1980-549720230021. eCollection 2023.
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.
描述一项基于人群的研究的初始基线结果，以及一项方案，以便评估不同机器学习算法的性能，目的是预测巴西南里奥格兰德州佩洛塔斯市城区成年人代表性样本对紧急和急救服务的需求。
该研究题为“佩洛塔斯（RS）急诊部使用和人工智能研究（EAI PELOTAS）”（https://wp.ufpel.edu.br/eaipelotas/）。2021 年 9 月至 12 月期间，对参与者进行了基线调查。计划在 12 个月后进行随访，以评估过去一年中紧急和急救服务的使用情况。之后，将测试机器学习算法以预测未来一年紧急和急救服务的使用情况。
共有 5722 名参与者回答了调查，大多数为女性（66.8%），平均年龄为 50.3 岁。家庭平均人口数为 2.6。样本中大多数为白人，且未完成小学或以下教育程度。约 30%的样本存在肥胖问题，14%患有糖尿病，39%患有高血压。
本文提出了一个方案，描述了为在未来一年内预测佩洛塔斯市居民对紧急和急救服务的需求而制作模型所采取的步骤。
