Discovering symptom patterns of COVID-19 patients using association rule mining. (April 2021) - Tandan, Meera; Acharya, Yogesh; Pokharel, Suresh; Timilsina, Mohan
Abstract: Background: The COVID-19 pandemic is a significant public health crisis that is hitting hard on people's health, well-being, and freedom of movement, and affecting the global economy. Scientists worldwide are competing to develop therapeutics and vaccines; currently, three drugs and two vaccine candidates have been given emergency authorization use. However, there are still questions of efficacy with regard to specific subgroups of patients and the vaccine's scalability to the general public. Under such circumstances, understanding COVID-19 symptoms is vital in initial triage; it is crucial to distinguish the severity of cases for effective management and treatment. This study aimed to discover symptom patterns and overall symptom rules, including rules disaggregated by age, sex, chronic condition, and mortality status, among COVID-19 patients. Methods: This study was a retrospective analysis of COVID-19 patient data made available online by the Wolfram Data Repository through May 27, 2020. We applied a widely used rule-based machine learning technique called association rule mining to identify frequent symptoms and define patterns in the rules discovered. Result: In total, 1, 560 patients with COVID-19 were included in the study, with a median age of 52 years. The most frequently occurring symptom was fever (67%), followed by cough (37%), malaise/body soreness (11%), pneumonia (11%), and sore throat (8%). Myocardial infarction, heart failure, and renal disease were present in less than 1% of patients. The top ten significant symptom rules (out of 71 generated) showed cough, septic shock, and respiratory distress syndrome as frequent consequents. If a patient had a breathing problem and sputum production, then, there was higher confidence of that patient having a cough; if cardiac disease, renal disease, or pneumonia was present, then there was a higher confidence of septic shock or respiratory distress syndrome. Symptom rules differed between younger and older patients and between male and female patients. Patients who had chronic conditions or died of COVID-19 had more severe symptom rules than those patients who did not have chronic conditions or survived of COVID-19. Concerning chronic condition rules among 147 patients, if a patient had diabetes, prerenal azotemia, and coronary bypass surgery, there was a certainty of hypertension. Conclusion: The most frequently reported symptoms in patients with COVID-19 were fever, cough, pneumonia, and sore throat; while 1% had severe symptoms, such as septic shock, respiratory distress syndrome, and respiratory failure. Symptom rules differed by age and sex. Patients with chronic disease and patients who died of COVID-19 had severe symptom rules more specifically, cardiovascular-related symptoms accompanied by pneumonia, fever, and cough as consequents. Highlights: COVID-19 is a public health crisis occurring worldwide. The efficacy of therapeutics and vaccines is still not well understood in some specific groups (e.g., elderly, and with clinical comorbidities). This study aims to discover COVID-19 symptom patterns and rules by applying association rule mining techniques. These rules would support the initial identification of severe COVID-19 cases based on symptom including the management and treatment. The most frequent symptoms were fever, cough, pneumonia, sore throat, while 1% had septic shock, respiratory distress syndrome, respiratory failure. Symptom rules patterns differ between younger and older COVID-19 patients and male and female patients. Patients with chronic disease and who died of COVID-19 had severe symptom rules, more specifically cardiovascular-related, accompanied by pneumonia, fever, and cough.