Purpose. COVID-19 pandemic has shown an urgent need for decision support systems to help doctors in a period of stress and uncertainty. However, significant differences in hospital conditions, as well as skepticism of doctors about machine learning algorithms, limit their introduction into clinical practice. Our goal was to test and apply a principle of "patient-like-mine" decision support in rapidly changing conditions of a pandemic.
Methods. In the developed system we implemented a fuzzy search that allows a doctor to compare their medical case with similar cases recorded since the beginning of the pandemic in their medical center. Various distance metrics were tried for obtaining clinically relevant search results. With the use of R programming language, we designed the first version of the system in approximately a week. A set of features for the comparison of the cases was selected with the use of random forest algorithm implemented in Caret. Shiny package was chosen for the design of GUI.
Results. The deployed tool allowed doctors to quickly estimate the current conditions of their patients by means of studying the most similar previous cases stored in the local health information system. Extensive testing of the system during the first wave of COVID-19 has shown that this approach helps not only to draw a conclusion about the optimal treatment tactics and to train medical staff in real-time but also to optimize patients’ individual testing plans.
Conclusions. This project points to the possibility of rapid prototyping and effective usage of "patient-like-mine" search systems at the time of a pandemic caused by a poorly known pathogen.