Predicting invasive mechanical ventilation in COVID 19 patients: A validation study.

Introduction: The decision to intubate and ventilate a patient is mainly clinical. Both delaying intubation (when needed) and unnecessarily invasively ventilating (when it can be avoided) are harmful. We recently developed an algorithm predicting respiratory failure and invasive mechanical ventilation in COVID-19 patients.

A novel machine learning model to predict respiratory failure and invasive mechanical ventilation in critically ill patients suffering from COVID-19.

In hypoxemic patients at risk for developing respiratory failure, the decision to initiate invasive mechanical ventilation (IMV) may be extremely difficult, even more so among patients suffering from COVID-19. Delayed recognition of respiratory failure may translate into poor outcomes, emphasizing the need for stronger predictive models for IMV necessity. We developed a two-step model; the first step was to train a machine learning predictive model on a large dataset of non-COVID-19 critically ill hypoxemic patients from the United States (MIMIC-III).

[Ranking scientific research projects based on the results of ballots in expert commissions].

A procedure for processing the secret ballot results in medical research project contests is proposed to make a generalized adjustment of research projects and their rating scales. The computing efficacy of the procedure makes it applicable in operative contest service.

[The provision of information for the work of expert councils in conducting competitions for scientific research projects].

The information and calculating system (ICS Konkurs) for accompaniment of the work of Expert Councils of the USSR AMS at competitions of medical research works projects, developed by an IBM, is examined. The system is of high efficiency, permits one to automate a great quantity of routine operations, is easily adaptive to work conditions of any Expert council. The ICS Konkurs can be used at any competitions and in the activities of Scientific Councils of Research Institutes.