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Related Experiment Video

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An R-Based Landscape Validation of a Competing Risk Model

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Contrastive Learning for Clinical Outcome Prediction with Partial Data Sources.

Meng Xia¹, Jonathan Wilson², Benjamin Goldstein²

¹Department of Electrical and Computer Engineering, Duke University, Durham, US.

Proceedings of Machine Learning Research

|August 16, 2024

Summary

We developed CLOPPS, a new machine learning method for predicting clinical outcomes using electronic health record (EHR) data. CLOPPS effectively handles situations where data sources differ between training and real-world use.

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Area of Science:

Machine Learning
Clinical Informatics
Biomedical Data Science

Background:

Machine learning models are increasingly used for predicting clinical outcomes from electronic health record (EHR) data.
A key limitation of existing models is the assumption of identical data source availability during training and inference.
Real-world deployment often involves partial data availability, posing a challenge for model generalizability.

Purpose of the Study:

To introduce Contrastive Learning for clinical Outcome Prediction with Partial data Sources (CLOPPS).
To develop a method that trains models to capture information across diverse data sources and can generalize to settings with limited data.
To improve the robustness and applicability of machine learning models in clinical outcome prediction.

Main Methods:

CLOPPS trains encoders to extract information from various data sources.
These encoders are then used to build classifiers that can operate with a single, potentially restricted, data source.
The approach is compatible with existing cross-sectional and longitudinal outcome classification models.

Main Results:

CLOPPS demonstrated superior performance compared to strong baseline models.
Experiments were conducted on two real-world datasets, validating the model's effectiveness.
The method consistently outperformed baselines across several practical scenarios involving partial data availability.

Conclusions:

CLOPPS offers a robust solution for clinical outcome prediction when data sources vary between training and inference.
The approach enhances the practical utility of machine learning models in real-world healthcare settings.
This method addresses a critical gap in applying predictive models to dynamic and incomplete EHR data.