We hosted a data science competition in collaboration with MAFAT to increase the reliability of radar tracks. We gathered leading machine learning experts to crack the case. Read about our process and our very interesting results!
In this post, we review a machine-learning-based model that was trained to predict the results of COVID-19 testing based on clinical symptoms, vital signs, and background data only. The model was trained on a high-quality clinical data repository that was collected by Carbon Health and Braid Health in California and that was published as an open dataset. The dataset is relatively small, including about 1,600 SARS-CoV-2 RNA RT-PCR tests. Nevertheless, the results are encouraging. The trained model predicts the actual test results with a ~70% probability, tested on a hold-out test set. Our work suggests that machine learning models could be included as part of routine screening for COVID-19 and can assist in prioritizing RT-PCR testing.