AUTHOR=Jamshidi Elham , Asgary Amirhossein , Tavakoli Nader , Zali Alireza , Dastan Farzaneh , Daaee Amir , Badakhshan Mohammadtaghi , Esmaily Hadi , Jamaldini Seyed Hamid , Safari Saeid , Bastanhagh Ehsan , Maher Ali , Babajani Amirhesam , Mehrazi Maryam , Sendani Kashi Mohammad Ali , Jamshidi Masoud , Sendani Mohammad Hassan , Rahi Sahand Jamal , Mansouri Nahal
TITLE=Symptom Prediction and Mortality Risk Calculation for COVID-19 Using Machine Learning
JOURNAL=Frontiers in Artificial Intelligence
VOLUME=4
YEAR=2021
URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2021.673527
DOI=10.3389/frai.2021.673527
ISSN=2624-8212
ABSTRACT=Background: Early prediction of symptoms and mortality risks for COVID-19 patients would improve healthcare outcomes, allow for the appropriate distribution of healthcare resources, reduce healthcare costs, aid in vaccine prioritization and self-isolation strategies, and thus reduce the prevalence of the disease. Such publicly accessible prediction models are lacking, however.
Methods: Based on a comprehensive evaluation of existing machine learning (ML) methods, we created two models based solely on the age, gender, and medical histories of 23,749 hospital-confirmed COVID-19 patients from February to September 2020: a symptom prediction model (SPM) and a mortality prediction model (MPM). The SPM predicts 12 symptom groups for each patient: respiratory distress, consciousness disorders, chest pain, paresis or paralysis, cough, fever or chill, gastrointestinal symptoms, sore throat, headache, vertigo, loss of smell or taste, and muscular pain or fatigue. The MPM predicts the death of COVID-19-positive individuals.
Results: The SPM yielded ROC-AUCs of 0.53–0.78 for symptoms. The most accurate prediction was for consciousness disorders at a sensitivity of 74% and a specificity of 70%. 2,440 deaths were observed in the study population. MPM had a ROC-AUC of 0.79 and could predict mortality with a sensitivity of 75% and a specificity of 70%. About 90% of deaths occurred in the top 21 percentile of risk groups. To allow patients and clinicians to use these models easily, we created a freely accessible online interface at www.aicovid.net.
Conclusion: The ML models predict COVID-19-related symptoms and mortality using information that is readily available to patients as well as clinicians. Thus, both can rapidly estimate the severity of the disease, allowing shared and better healthcare decisions with regard to hospitalization, self-isolation strategy, and COVID-19 vaccine prioritization in the coming months.