Mapping Health Trajectories on Self Organizing Maps using COVID-19 Patient's Blood Tests

ABSTRACT

Since COVID-19 appeared in December 2019, scientists are researching new ways to improve the management of the disease. Considering machine learning approaches have proven to be very useful tools to discover hidden patterns in data, we propose in this paper to apply a Self Organizing Map (SOM) to characterize the health-status evolution of COVID-19 patients. The SOM is a neural network whose neurons can be represented as cells in a bi-dimensional grid preserving the mapping from the original space to the map units. We consider real-world data of hospitalized COVID-19 patients in a Spanish hospital during the first wave of the pandemic. Patients are represented by six blood tests (leukocytes and D-dimer, among others) in a daily basis. Besides, each patient is associated with one of two different health-status favorable evolution (discharged home) and unfavorable evolution (exitus or admission to the intensive care unit). We show the potential of our approach by detailing the mapping of the health trajectory associated with different particular cases and drawing their trajectory on the bi-dimensional map of the SOM. © 2021 IEEE.