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首页> 外文会议>IEEE EMBS International Conference on Biomedical and Health Informatics >Machine Learning Prediction of Hospitalization due to COVID-19 based on Self-Reported Symptoms: A Study for Brazil
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Machine Learning Prediction of Hospitalization due to COVID-19 based on Self-Reported Symptoms: A Study for Brazil

机译：基于自我报告的症状的Covid-19由于Covid-19而入院的机器学习预测：巴西研究

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Predicting the need for hospitalization due to COVID-19 may help patients to seek timely treatment and assist health professionals to monitor cases and allocate resources. We investigate the use of machine learning algorithms to predict the risk of hospitalization due to COVID-19 using the patient’s medical history and self-reported symptoms, regardless of the period in which they occurred. Three datasets containing information regarding 217,580 patients from three different states in Brazil have been used. Decision trees, neural networks, and support vector machines were evaluated, achieving accuracies between 79.1% to 84.7%. Our analysis shows that better performance is achieved in Brazilian states ranked more highly in terms of the official human development index (HDI), suggesting that health facilities with better infrastructure generate data that is less noisy. One of the models developed in this study has been incorporated into a mobile app that is available for public use.
机译：预测因Covid-19由于Covid-19而期间的需求可以帮助患者寻求及时治疗，协助卫生专业人员监测案件并分配资源。 我们调查了机器学习算法的使用，以预测使用患者的病史和自我报告的症状导致的住院风险，无论它们发生的时期如何。 已经使用了三个数据集，其中包含有关巴西三种不同状态的217,580名患者的信息。 评估决策树，神经网络和支持向量机，在79.1％至84.7％之间取得准确度。 我们的分析表明，巴西国家在巴西州达到更好的表现，就官方人类发展指数（HDI）排名更高，这表明具有更好的基础设施的卫生设施产生不太嘈杂的数据。 本研究开发的其中一个模型已被纳入可用于公共使用的移动应用程序。

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