Analysis of COVID-19 Patients' Symptoms and Vaccine Impact Using Deep Learning Approach, and Development Machine Learning Based Risk Calculator: A Multicentric Collaborative Study

Background: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a global pandemic that has affected millions of people worldwide. This study aims to bridge the knowledge gap between acute and chronic symptoms, vaccination impact, and associated factors in patients across different low-income countries.
Methods: The study included 2,445 participants aged 18 years and older, testing positive for COVID-19. Data collection involved screening for medical histories, testing records, symptomatology, and persistent symptoms. Validated instruments, including the DePaul Symptom Questionnaire (DSQ-2) and Patient Health Questionnaire-9 (PHQ-9), were used. We applied a self-supervised and unsupervised deep neural network to extract features from the questionnaire. Gradient boosted machines (GMB) model was used to build a risk calculator for chronic fatigue, depression, and prolonged COVID-19 symptoms. The best-performing models were implemented in a shiny app and deployed online at: [https//ahmedshaheen.shinyapps.io/shaheen-covid-19/]. Also, there is an offline version of the application that can be downloaded: [link].
Findings: Out of the study cohort, 69.5% of the patients had symptoms lasting longer than 2 weeks. The most frequent symptoms were loss of smell 46.8%, dry cough (40.1%), loss of taste (37.8%), headaches (37.2%), and sore throat (28.9%). The patients also reported high rates of depression (47.7%), chronic fatigue (6.5%), and infection after vaccination (24.2%). Factors associated with chronic fatigue syndrome included sex, age, and smoking. Vaccinated individuals demonstrated lower odds of experiencing prolonged COVID-19 symptoms, chronic fatigue syndrome, and depression. The predictive models achieved a high area under the receiver operating characteristic curve (AUC) scores of 0.87, 0.82, and 0.74, respectively.
Interpretation: The results provide insights into the consequences of COVID-19 and a predictive tool to understand factors influencing depression, chronic fatigue syndrome, and prolonged COVID-19 symptoms. The study reveals variables affecting these outcomes and the interplay between pre-existing conditions, treatments, and the duration of symptoms post-recovery.