Preventing dataset shift from breaking machine-learning biomarkers.

Area of Science:

• Biomedical research
• Machine learning
• Biomarker discovery

Background:

• Machine learning (ML) offers potential for novel biomarker discovery from extensive biomedical data.
• Biomarker reliability is compromised when data used for extraction differs from the target population, a phenomenon known as dataset shift.
• Dataset shifts are common in biomedical research due to factors like recruitment biases.

Purpose of the Study:

• To explain how dataset shifts disrupt the performance of ML-extracted biomarkers.
• To provide an overview of methods for detecting and correcting dataset shifts in biomarker validation.

Main Methods:

• Review of literature on dataset shift in machine learning for biomarker discovery.
• Analysis of mechanisms by which dataset shifts affect biomarker performance.
• Exploration of existing and potential strategies for dataset shift detection and mitigation.

Main Results:

• Dataset shifts, particularly those arising from differing cohort characteristics, can lead to unreliable biomarkers.
• Standard ML techniques are insufficient for biomarker validation when dataset shifts are present.
• Effective detection and correction strategies are crucial for robust biomarker application.

Conclusions:

• Addressing dataset shift is critical for the successful translation of ML-derived biomarkers into clinical practice.
• Further research into robust methods for biomarker extraction and validation across diverse populations is warranted.
• Understanding and mitigating dataset shift will enhance the reliability and utility of biomarkers in biomedical applications.