Multi-view representation learning for tabular data integration using inter-feature relationships
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View abstract on PubMed
Summary
This summary is machine-generated.Harmonizing data sources without metadata is crucial for robust algorithms. Inter-feature relationships effectively map features across datasets, with contrastive learning showing superior performance in matching and reconstruction.
Area Of Science
- Data Science
- Machine Learning
- Bioinformatics
Background
- Harmonizing diverse data sources is a key challenge in data science, particularly in healthcare.
- Integrating data from multiple origins with unmapped features is essential for developing generalizable algorithms.
- Existing methods often rely on ambiguous or unavailable metadata, necessitating new approaches.
Purpose Of The Study
- To design and evaluate methods for mapping structured tabular datasets from electronic health records (EHRs) independent of metadata.
- To identify effective strategies for feature mapping when only a small set of features are initially known.
Main Methods
- Comparison of contrastive learning, partial auto-encoders, mutual-information graph optimizers, and statistical baselines.
- Evaluation on simulated data, public datasets, MIMIC-III, and perioperative records.
- Performance assessment based on feature mapping accuracy and data reconstruction.
Main Results
- Contrastive learning methods demonstrated superior performance in feature matching and reconstruction, especially on real-world data.
- Partial auto-encoders performed comparably to contrastive methods in many scenarios.
- A novel statistical method showed reasonable performance with less hyperparameter tuning.
Conclusions
- Inter-feature relationships are effective for identifying matching features across tabular datasets lacking metadata.
- Decoder architectures can effectively impute features when exact matches are not found.
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