Intensive care medicine is complex, resource intensive and expensive. It is a dynamic and highly technical field of medicine, taking care of the sickest patients. Decisions need to be made rapidly based on the evolving clinical state of the patient which can fluctuate over seconds and minutes. We develop ML models to tackle major predictive challenges for critically-ill patients. Specifically, models that predict an upcoming possible adverse event to provide the clinical team time to intervene and thus improve outcome and save lives, and models that predict the future course and treatment response in a patient-specific manner.
Pulse oximetry is routinely used for monitoring patient’s oxygen saturation level non-invasively. A low oxygen level in the blood means low oxygen in the tissues and ultimately this can lead to organ failure. The development of digital oximetry biomarkers (OBM) engineered from the oxygen saturation time series can support diagnosis, characterize subgroups of patients with various disease severity (phenotyping) and enable continuous monitoring of patient’s pulmonary function to predict eventual deteriorations (prognosis). We create new OBM and ML models for the diagnosis of respiratory conditions such as obstructive sleep apnea, chronic obstructive pulmonary disease and pneumonia.
Major cardiovascular and cerebrovascular events occur in individuals without known pre-existing cardiovascular conditions. Preventing such events remains a serious public health challenge. For that purpose, clinical risk scores can be used to identify individuals with high cardiovascular risks. However, available scoring scales have shown moderate performance. Despite being part of the routine evaluation of many patients in both primary and specialized care, the role of electrocardiogram (ECG) analysis in cardiovascular disease prediction and, hence, prevention is not as clear. We research digital biomarkers and deep representation learning approaches to cardiovascular diseases risk prediction using the ECG.