From Real-World Patient Data to Individualized Treatment Effects Using Machine Learning: Current and Future Methods...

Area of Science:

• Biomedical Informatics
• Machine Learning
• Causal Inference

Background:

• Randomized control trials (RCTs) establish drug efficacy but lack patient heterogeneity data.
• Electronic health records (EHRs) capture diverse patient data and treatment responses.
• Individualized treatment decisions require evidence of patient-specific benefits.

Purpose of the Study:

• To explore opportunities and challenges of using observational data for machine learning-based individualized treatment effect estimation.
• To describe state-of-the-art machine learning methods for causal inference in treatment effect estimation.
• To highlight future research for leveraging EHRs and machine learning in personalized treatment recommendations.

Main Methods:

• Utilizing machine learning for causal inference on observational data (EHRs).
• Describing cross-sectional and longitudinal modeling approaches for treatment effect estimation.
• Integrating experimental data (RCTs) and quantitative pharmacology models.

Main Results:

• Machine learning methods can estimate individualized treatment effects from EHR data.
• Observational data offers insights into heterogeneous patient responses to treatments.
• Future research directions focus on validating and improving ML models with diverse data sources.

Conclusions:

• EHRs and machine learning hold significant potential for personalized treatment recommendations.
• Collaboration across disciplines is crucial to integrate RCTs, disease models, and EHR-based ML.
• Combining diverse data sources will optimize individualized treatment strategies.