Dear All,
We are urgently looking for students interested in doing a master's thesis on data collection and processing in cooperation with the intensive care unit of our hospital. Please find below the project description.
Applicants are required to have a strong background in machine learning and data analysis, independent thinking and self-organization skills. Feel free to spread the voice among mates who might be interested.
Best regards
Andrea Zanella & Federico Mason
Predicting SBT outcomes in intensive care
In hospital intensive care units, ventilator machines are used to support breathing functions in patients who are in critical condition. When his/her clinical status improves, the patient’s readiness to breathe autonomously is verified via a medical procedure called Spontaneous Breathing Trial (SBT). If the procedure concludes successfully, the patient is extubated and the ventilator machine can be allocated to a new user. On the other hand, performing an SBT procedure on a patient who is not ready has critical consequences on the patient's health and, thus, accurately selecting the candidate is extremely important. Such a decision must be carried out by analyzing the patient's health parameters during the hours before the planned time of the procedure. However, nowadays, clinical practices do not provide clear indications for such a goal. The project goal is to analyze the clinical data collected by the ventilator machines of the intensive care unit of Padova's hospital and design an algorithm that extracts new clinical biomarkers to predict SBT success probability.