Machine Learning for Clinical Outcome Prediction.

Area of Science:

• Clinical informatics
• Biomedical data science
• Machine learning in healthcare

Background:

• Machine learning (ML) is increasingly used in clinical decision-making.
• ML models are prevalent in literature for predicting patient outcomes like mortality and acute kidney injury.
• Electronic health records (EHRs) are a primary data source for these predictive models.

Purpose of the Study:

• To review the state-of-the-art in machine learning for outcome prediction using EHR data.
• To cover key aspects including data processing, inference, and model evaluation.
• To identify limitations and future research directions in this field.

Main Methods:

• Systematic review of recent literature on ML-based outcome prediction models.
• Analysis of methodologies for data processing from EHRs.
• Evaluation of inference techniques and model performance metrics.

Main Results:

• Summary of current ML applications in predicting diverse clinical outcomes.
• Identification of common data processing, inference, and evaluation strategies.
• Discussion of limitations in current modeling assumptions.

Conclusions:

• Machine learning shows significant potential in enhancing clinical decision-making through outcome prediction.
• Further research is needed to address limitations in modeling assumptions and improve model generalizability.
• Future work should focus on robust data handling, advanced inference, and comprehensive model evaluation for EHR-derived predictions.