Machine Learning for Benchmarking Critical Care Outcomes.

Area of Science:

• Critical care medicine
• Artificial intelligence
• Health informatics

Background:

• Critical care efficacy relies on system evaluation and improvement.
• Benchmarking, a comparative analysis against standards, aids in identifying areas for improvement.
• Machine learning (ML) models have advanced clinical outcome prediction over the past two decades.

Purpose of the Study:

• To review key discoveries and outcomes in ML for critical care benchmarking.
• To guide clinicians and researchers in selecting optimal ML methodologies.
• To highlight advancements in predicting critical care outcomes like mortality, length of stay, and mechanical ventilation.

Main Methods:

• A narrative review of literature from 2003-2023 using PubMed and Google Scholar.
• Searched for predictive models utilizing ML for mortality, length of stay, and mechanical ventilation.
• Manually curated articles for a comprehensive reader perspective.

Main Results:

• ML effectively addresses critical care outcome prediction challenges.
• Advancements noted in feature engineering, data preprocessing, model selection, and validation.
• ML models have shown success in handling nonlinear relationships, class imbalances, missing data, and documentation variability.

Conclusions:

• ML offers novel tools for enhancing critical care outcome benchmarking.
• Further research is required in areas such as class imbalance, fairness, calibration, and generalizability.
• Long-term validation of published ML models in critical care is essential.