A machine learning algorithm to increase COVID-19 inpatient diagnostic capacity

• Published in: PloS one Vol. 15; no. 9; p. e0239474
• Main Authors: Goodman-Meza, David, Rudas, Akos, Chiang, Jeffrey N, Adamson, Paul C, Ebinger, Joseph, Sun, Nancy, Botting, Patrick, Fulcher, Jennifer A, Saab, Faysal G, Brook, Rachel, Eskin, Eleazar, An, Ulzee, Kordi, Misagh, Jew, Brandon, Balliu, Brunilda, Chen, Zeyuan, Hill, Brian L, Rahmani, Elior, Halperin, Eran, Manuel, Vladimir
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 22.09.2020; Public Library of Science (PLoS)
• Subjects: Adult; Aged; Algorithms; Area Under Curve; Betacoronavirus; Biological markers; Biology and life sciences; Blood; Cardiology; Clinical Laboratory Techniques - methods; Clinical Laboratory Techniques - standards; Computer and Information Sciences; Computer science; Confidence intervals; Coronavirus Infections - diagnosis; Coronaviruses; COVID-19; COVID-19 Testing; Diagnosis; Diagnostic systems; Emergency medical care; Emergency medical services; Geffen, David; Health aspects; Hospitals; Humans; Identification and classification; Infectious diseases; Inpatients; Laboratories; Learning; Learning algorithms; Los Angeles; Lymphocytes; Machine Learning; Mass Screening - methods; Mass Screening - standards; Medical diagnosis; Medicine; Medicine and Health Sciences; Middle Aged; Neutrophils; Pandemics; Physical Sciences; Pneumonia, Viral - diagnosis; Polymerase Chain Reaction; Proteins; Public health; Research and Analysis Methods; Retrospective Studies; SARS-CoV-2; Severe acute respiratory syndrome; Severe acute respiratory syndrome coronavirus 2; Standard deviation; Supervision; Los Angeles; United States; Los Angeles California; United States > US; California
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0239474

Worldwide, testing capacity for SARS-CoV-2 is limited and bottlenecks in the scale up of polymerase chain reaction (PCR-based testing exist. Our aim was to develop and evaluate a machine learning algorithm to diagnose COVID-19 in the inpatient setting. The algorithm was based on basic demographic and laboratory features to serve as a screening tool at hospitals where testing is scarce or unavailable. We used retrospectively collected data from the UCLA Health System in Los Angeles, California. We included all emergency room or inpatient cases receiving SARS-CoV-2 PCR testing who also had a set of ancillary laboratory features (n = 1,455) between 1 March 2020 and 24 May 2020. We tested seven machine learning models and used a combination of those models for the final diagnostic classification. In the test set (n = 392), our combined model had an area under the receiver operator curve of 0.91 (95% confidence interval 0.87-0.96). The model achieved a sensitivity of 0.93 (95% CI 0.85-0.98), specificity of 0.64 (95% CI 0.58-0.69). We found that our machine learning algorithm had excellent diagnostic metrics compared to SARS-CoV-2 PCR. This ensemble machine learning algorithm to diagnose COVID-19 has the potential to be used as a screening tool in hospital settings where PCR testing is scarce or unavailable.