Grant: Alliance Grants

When injury or illness severely compromises vital bodily functions, life-supporting interventions are provided in specialized hospital units called Intensive Care Unit (ICU). Patient management in ICU is complex and physicians make life-critical decisions by analyzing rapidly changing patient data. Current ICU prognostic tools only provide a prediction of hospital mortality, hence are not useful to predict clinical outcomes during the patient's ICU stay. Due to the limitations of current prognostic tools, ICU clinical making leads to non-standardized patient care and inefficient ICU resource utilization. In partnership with (a) Nova Scotia Health Authority, Department of Critical Care, (b) Nova Scotia (NS) Department of Health and Wellness, and (c) Doctors Nova Scotia, this project aims to develop novel ICU prognostic models, based on Canadian ICU data, to help ICU physicians make effective care decisions at clinically important time points to account for changes in the patient's condition during the patient's entire ICU stay. The project will investigate Deep Learning (DL) methods to develop ICU outcome prediction models to predict clinical outcomes at 5 clinically important time points-i.e. at time of admission, after 24, 48 and 72 hours, and 24 hours before discharge. We propose to address two key challenges-i.e. (a) progressive update of the prediction models to ensure predictive relevance to new ICU data; and (b) temporal probabilistic prediction of clinical outcomes across different time points during the patient's ICU stay. We will integrate multiple patient data sources from Nova Scotia health Authority Central Zone (NSHA-CZ) ICUs in Halifax-i.e. ICU clinical data, pathology data and radiologic data. We plan to apply explainable AI methods to provide clinically meaningful explanations of the predicted outcome at the attribute and conceptual levels. The prediction models will be prospectively evaluated over a 1-year period based on new ICU cases at NSHA-CZ to measure the clinical consequences of the model's predictions. The project's will deliver a prototype ICU Prognostic and Planning System (ICU-PPS) incorporating the prediction models developed for NSHA-CZ ICUs.