A machine learning model to identify early stage symptoms of SARS-Cov-2 infected patients
作者：
Highlights：
• Machine learning was used to develop models to predict COVID-19 positive patient.
• Features were extracted from patient data using string matching algorithms.
• Constructed a novel dataset from unstructured hospitalized patient information.
• Used descriptive statistical analysis for frequency calculation of patient symptoms.
• Identified significant symptoms of COVID-19 patients using five different ML models.
摘要
•Machine learning was used to develop models to predict COVID-19 positive patient.•Features were extracted from patient data using string matching algorithms.•Constructed a novel dataset from unstructured hospitalized patient information.•Used descriptive statistical analysis for frequency calculation of patient symptoms.•Identified significant symptoms of COVID-19 patients using five different ML models.
论文关键词：SARS-Cov-2,COVID-19,Coronavirus,Machine learning,Early stage symptom
论文评审过程：Received 18 April 2020, Revised 7 June 2020, Accepted 12 June 2020, Available online 20 June 2020, Version of Record 30 June 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113661
