The global surge in COVID-19 cases underscores the need for fast, scalable, and reliable testing. Current COVID-19 diagnostic tests are limited by turnaround time, limited availability, or occasional false findings. Here, we developed a machine learning-based framework for predicting individual COVID-19 positive diagnosis relying only on readily-available baseline data, including patient demographics, comorbidities, and common lab values. Leveraging a cohort of 31,739 adults within an academic health system, we trained and tested multiple types of machine learning models, achieving an area under the curve of 0.75. Feature importance analyses highlighted serum calcium levels, temperature, age, lymphocyte count, smoking, hemoglobin levels, aspartate aminotransferase levels, and oxygen saturation as key predictors. Additionally, we developed a single decision tree model that provided an operable method for stratifying sub-populations. Overall, this study provides a proof-of-concept that COVID-19 diagnosis prediction models can be developed using only baseline data. The resulting prediction can complement existing tests to enhance screening and pandemic containment workflows.
【저자키워드】 Infectious diseases, machine learning, Programming language, 【초록키워드】 COVID-19, pandemic, diagnostic test, Comorbidities, Diagnosis, oxygen, complement, predictors, smoking, Lymphocyte count, lymphocyte, oxygen saturation, Cohort, diagnostic tests, Patient, COVID-19 diagnosis, Aspartate aminotransferase, age, temperature, hemoglobin, health system, Decision tree, calcium, Analysis, machine learning models, demographics, an area, COVID-19 case, aspartate, positive, serum calcium levels, serum calcium, current, ENhance, resulting, tested, provided, provide, baseline, lab values, operable, 【제목키워드】 prediction, COVID-19 diagnosis, demographics, lab, baseline,