Development, evaluation and validation of machine learning algorithms to detect atypical and asymptomatic presentations of Covid-19 in hospital practice.

Baktash, Vadir; Hosack, Tom; Rule, Rory; Patel, Nishil; Kho, Jason; Sekhar, Ravi; Mandal, Amit K J; Missouris, Constantinos G

BACKGROUND Diagnostic methods for Covid-19 have improved, both in speed and availability. Because of atypical and asymptomatic carriage of the virus and nosocomial spread within institutions, timely diagnosis remains a challenge. Machine learning models trained on blood test results have shown promise in identifying cases of Covid-19. AIMS To train and validate a machine learning model capable of differentiating Covid-19 positive from negative patients using routine blood tests and assess the model's accuracy against atypical and asymptomatic presentations. DESIGN AND METHODS We conducted a retrospective analysis of medical admissions to our institution during March and April 2020. Participants were categorised into Covid-19 positive or negative groups based on clinical, radiological features or nasopharyngeal swab. A machine learning model was trained on laboratory parameters and validated for accuracy, sensitivity and specificity and externally validated at an unconnected establishment. RESULTS An Ensemble Bagged Tree model was trained on data collected from 405 patients (212 Covid-19 positive) producing accuracy of 81.79% (95% confidence interval (CI) 77.53% to 85.55%), sensitivity of 85.85% (CI 80.42% to 90.24%) and specificity of 76.65% (CI 69.49% to 82.84%). Accuracy was preserved for atypical and asymptomatic subgroups. Using an external data set for 226 patients (141 Covid-19 positive) accuracy of 76.82% (CI 70.87% to 82.08%), sensitivity of 78.38% (CI 70.87% to 84.72%) and specificity of 74.12% (CI 63.48% to 83.01%) was achieved. CONCLUSION A machine learning model using routine laboratory parameters can detect atypical and asymptomatic presentations of Covid-19, and might be an adjunct to existing screening measures.