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.
Zhang, J., Jun, T., Frank, J. et al. Prediction of individual COVID-19 diagnosis using baseline demographics and lab data. Sci Rep 11, 13913 (2021). https://doi.org/10.1038/s41598-021-93126-7
- python>=3.7.6
- scikit-learn==0.22.2
- graphviz==0.10.1
- numpy==1.18.5
- pandas==1.1.3
- scipy==1.4.1
- matplotlib==3.2.2
- seaborn==0.11.0
- eli5==0.10.1 (optional)
Main python notebook for data analysis, machine learning model development + hyperparameter optimization, and figure visualization.
Python notebook for COVID-19 antibody test results analysis (not included in main study). Will update when more antibody data becomes available.
Mount Sinai Data Warehouse: 31,739 adult patients with RT-PCR test results up to June 2nd, 2020
Feature importances plots, ROC curves, probability distribution, and simple decision tree.