COVID-19 prognostic model using Bayesian networks learnt on patient data

Abstract

The response to the ongoing second wave of the COVID-19 pandemic can be helped by giving medical professionals access to models learned on patient data. To achieve this, we learned a Bayesian network model to predict risk of ICU admission, death and time of stay in the hospital from patient history, initial vital signs, initial laboratory tests and medication. Data were obtained from patients that were admitted to an HM hospital with suspicion of COVID-19 until 24/04/2020, excluding unconfirmed diagnosis, those who were admitted before the epidemic started in Madrid, had an outcome that was not discharge or death or died within 24 hours of presentation. Relevant variables for the model were selected with help from medical professionals. We learned the model using Bayesian search as implemented in GeNIe. Of 2,307 patients in the dataset, 679 were excluded. With the remaining 1,645 patients, we learned a model that predicted death with 86.4% accuracy. Some of the initial variables were discarded because they were independent of the outcomes of interest conditioned on some of the other variables. This high redundancy might be useful to build simpler tests for the severity of COVID-19. We show how the model can be used at different stages of patient admission and even with only partial information about the patient. This can be done by clinicians that want a fast second opinion or a summary of the available data from previous patients similar to the one at hand. We then include how we plan to improve the model with extra patient data and how it could be expanded to other contexts, like for example, an epidemiological one.