论文标题
通过随机决策树的实验室测试和X射线数据的Covid-19患者的预后预测
Prognosis Prediction in Covid-19 Patients from Lab Tests and X-ray Data through Randomized Decision Trees
论文作者
论文摘要
AI和机器学习可以提供强大的工具来帮助与Covid-19的斗争。在本文中，我们介绍了一项研究和基于机器学习的混凝土工具，以预测住院患者的COVID-19患者的预后。特别是，我们解决了预测住院不同时间患者死亡风险的任务，在某些人口统计信息，胸部X射线分数和一些实验室发现的基础上。我们的机器学习模型使用了使用来自2000多名患者的数据进行培训和测试的决策树的集合。对模型的实验评估显示在解决解决任务时表现出色。
AI and Machine Learning can offer powerful tools to help in the fight against Covid-19. In this paper we present a study and a concrete tool based on machine learning to predict the prognosis of hospitalised patients with Covid-19. In particular we address the task of predicting the risk of death of a patient at different times of the hospitalisation, on the base of some demographic information, chest X-ray scores and several laboratory findings. Our machine learning models use ensembles of decision trees trained and tested using data from more than 2000 patients. An experimental evaluation of the models shows good performance in solving the addressed task.
