Learning Patient Representations from Text
Open accessAbstract
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.
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For both structured codes and unstructured notes, standardized vocabularies including FHIR and UMLS CUIs are introduced to transfer raw tokens into unique phrases to reduce sparsity.
“For both structured codes and unstructured notes, standardized vocabularies including FHIR [6] and UMLS CUIs [62,63] are introduced to transfer raw tokens into unique phrases to reduce the sparsity of the input data.”
From Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review (2021) · cites this paper as [62]
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Self-citation
- Authors
- Dligach, D., & Miller, T.
- Affiliations
- Loyola University Chicago (US)
- Boston Children's Hospital (US)
- Venue
- Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics
- Published
- 2018
- Pages
- 119-123
- OpenAlex
- W2799774807
- Cited by
- 13
- References
- 10
- Citations by year
- 2025: 2 · 2022: 3 · 2021: 2 · 2020: 2 · 2019: 4
- FWCI
- 0.82
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Joint Conference on Lexical and Computational Semantics (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Nvidia
- National Institutes of Health
- National Institute of General Medical Sciences (R01GM114355)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
- Keywords
- patient representation learning, phenotyping, electronic health records, comorbidity detection, artificial neural networks, clinical text
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