Leveraging interpretable machine learning algorithms to predict postoperative patient outcomes on mobile devices.

Area of Science:

• Medical Informatics
• Machine Learning in Healthcare
• Surgical Risk Assessment

Background:

• Accurate patient risk estimation is vital for managing postoperative expectations and care.
• Traditional risk models often lack interpretability, hindering clinical trust and adoption.
• Machine learning (ML) offers advanced methods for capturing complex preoperative risk factors.

Purpose of the Study:

• To review state-of-the-art machine learning approaches for postoperative risk estimation.
• To highlight the development of explainable risk calculators.
• To discuss the clinical application and future integration of ML in healthcare.

Main Methods:

• Review of modern machine learning models for risk prediction.
• Focus on models that provide explainable risk estimates.
• Analysis of applications in clinical settings.

Main Results:

• Modern ML models can accurately predict surgical patient risk.
• Explainable AI (XAI) techniques enhance the interpretability of ML risk predictions.
• These advanced models overcome the limitations of older, non-interpretable methods.

Conclusions:

• Explainable ML models improve accuracy and trustworthiness in postoperative risk assessment.
• Successful clinical applications demonstrate the value of interpretable AI in surgery.
• Systematic integration of ML holds significant potential for broader healthcare applications.