Integrating biomedical research and electronic health records to create knowledge-based biologically meaningful machine-readable embeddings
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Abstract
In order to advance precision medicine, detailed clinical features ought to be described in a way that leverages current knowledge. Although data collected from biomedical research is expanding at an almost exponential rate, our ability to transform that information into patient care has not kept at pace. A major barrier preventing this transformation is that multi-dimensional data collection and analysis is usually carried out without much understanding of the underlying knowledge structure. Here, in an effort to bridge this gap, Electronic Health Records (EHRs) of individual patients are connected to a heterogeneous knowledge network called Scalable Precision Medicine Oriented Knowledge Engine (SPOKE). Then an unsupervised machine-learning algorithm creates Propagated SPOKE Entry Vectors (PSEVs) that encode the importance of each SPOKE node for any code in the EHRs. We argue that these results, alongside the natural integration of PSEVs into any EHR machine-learning platform, provide a key step toward precision medicine.
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EHRs are often supplemented with other modalities such as diseases, symptoms, molecular data, drug interactions, etc.
“EHRs are often supplemented with other modalities, such as diseases, symptoms, molecular data, drug interactions, etc [7, 206, 211, 212].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [7]
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- Authors
- Nelson, C. A., Butte, A. J., & Baranzini, S. E.
- Affiliations
- University of California, San Francisco (US)
- Corresponding author
- Baranzini, Sergio E.
- Venue
- Nature Communications
- Published
- 2019-07-10
- Volume
- 10
- Issue
- 1
- Pages
- 3045-3045
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC6620318
- OpenAlex
- W2961798429
- Cited by
- 85
- References
- 47
- Citations by year
- 2026: 12 · 2025: 9 · 2024: 14 · 2023: 17 · 2022: 11 · 2021: 11 · 2020: 10 · 2019: 1
- FWCI
- 3.76
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institutes of Health (UL1TR001872)
- Genentech
- University of California, San Francisco (UL1 TR001872)
- National Institute of General Medical Sciences (T32GM067547)
- National Center for Advancing Translational Sciences (TR001872)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Keywords
- electronic health records, precision medicine, knowledge graphs, biomedical knowledge integration, unsupervised learning, node embeddings
- MeSH terms
- Unsupervised Machine Learning, Data Analysis, Data Collection, Biomedical Research, Precision Medicine, Electronic Health Records
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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