Causally-informed deep learning towards explainable and generalizable outcome prediction in critical care

Area of Science:

• Artificial Intelligence in Medicine
• Critical Care Medicine
• Machine Learning

Background:

• Deep learning models are revolutionizing critical care medicine with accurate early warning systems.
• Conventional deep learning models have limitations in clinical implementation, including opaque decision-making and poor generalizability.
• Predicting clinical deteriorations like acute kidney injury and myocardial infarction is crucial in critical care.

Purpose of the Study:

• To develop a causally-informed deep learning framework for improved clinical prediction and interpretation.
• To address the limitations of opacity and generalizability in conventional deep learning models for critical care.
• To enhance clinical decision support through reliable forecasting and transparent physiological interpretation.

Main Methods:

• A causally-informed deep learning framework integrating causal discovery with prediction was developed.
• The framework jointly identifies causal drivers of clinical outcomes and predicts patient deterioration.
• The approach was evaluated for accuracy and generalizability across diverse patient groups and critical care environments.

Main Results:

• The causally-informed deep learning framework demonstrated superior accuracy in predicting six different critical deteriorations.
• The approach exhibited improved generalizability across diverse patient groups compared to baseline algorithms.
• Explicit causal pathways were identified, serving as references for clinical diagnosis and interventions.

Conclusions:

• Incorporating causal reasoning into deep learning enhances clinical prediction and decision support in critical care.
• The developed framework offers transparent physiological interpretation alongside reliable forecasting.
• This approach paves the way for more trustworthy AI implementation in critical care settings.