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Learning Patient Representations from Text
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Mining electronic health records for patients who satisfy a set of predefined criteria is known in medical informatics as phenotyping. Phenotyping has numerous applications such as outcome prediction, clinical trial recruitment, and retrospective studies. Supervised machine learning for phenotyping typically relies on sparse patient representations such as bag-of-words. We consider an alternative that involves learning patient representations. We develop a neural network model for learning patient representations and show that the learned representations are general enough to obtain state-of-the-art performance on a standard comorbidity detection task.
Forward citations
Cited by 2 Pith papers
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Two-stage Federated Phenotyping and Patient Representation Learning
Federated training of patient representations and phenotyping classifiers on clinical notes performs comparably to centralized training and better than training at one site.
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TAPER: Time-Aware Patient EHR Representation
TAPER embeds medical codes with a time-masked transformer and clinical notes with BERT, then concatenates the two with demographics to improve ICU mortality, readmission, and length-of-stay prediction on MIMIC-III.
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