Modeling electronic health record data using an end-to-end knowledge-graph-informed topic model Article Swipe
Yuesong Zou
,
Ahmad Pesaranghader
,
Aman Verma
,
David L. Buckeridge
,
Yue Li
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-1766545/v1
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-1766545/v1
The rapid growth of electronic health record (EHR) datasets opens up promising opportunities to understand human diseases in a systematic way. However, effective extraction of clinical knowledge from the EHR data has been hindered by its sparsity and noisy information. We present GAT-ETM, an end-to-end knowledge graph-based multimodal embedded topic model. GAT-ETM distills latent disease topics from EHR data by learning the embedding from a constructed medical knowledge graph. We applied GAT-ETM to a large-scale EHR dataset consisting of over 1 million patients. We evaluated its performance based on EHR reconstruction and drug imputation. GAT-ETM demonstrated superior performance over the alternative methods on both tasks. Moreover, our model learned clinically meaningful graph-informed embedding of the EHR codes. In additional, our model is also able to discover interpretable and accurate patient representations for patient stratification and drug recommendations. Our code is available at Anonymous GitHub.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-1766545/v1
- https://www.researchsquare.com/article/rs-1766545/latest.pdf
- OA Status
- green
- Cited By
- 1
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4293058541
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4293058541Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-1766545/v1Digital Object Identifier
- Title
-
Modeling electronic health record data using an end-to-end knowledge-graph-informed topic modelWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-06-27Full publication date if available
- Authors
-
Yuesong Zou, Ahmad Pesaranghader, Aman Verma, David L. Buckeridge, Yue LiList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-1766545/v1Publisher landing page
- PDF URL
-
https://www.researchsquare.com/article/rs-1766545/latest.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.researchsquare.com/article/rs-1766545/latest.pdfDirect OA link when available
- Concepts
-
Computer science, Knowledge graph, Embedding, Graph, Health records, Information retrieval, Data mining, End-to-end principle, Imputation (statistics), Machine learning, Data science, Artificial intelligence, Missing data, Theoretical computer science, Health care, Economics, Economic growthTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
- References (count)
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36Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2742491462, https://openalex.org/W2511950764, https://openalex.org/W3042895962, https://openalex.org/W2584780866, https://openalex.org/W3027889410, https://openalex.org/W3159614932, https://openalex.org/W2120757740, https://openalex.org/W1863753810, https://openalex.org/W2190089959, https://openalex.org/W2030597870, https://openalex.org/W242743621, https://openalex.org/W1970250140, https://openalex.org/W1808652302, https://openalex.org/W2982546097, https://openalex.org/W2951441387, https://openalex.org/W2517259736, https://openalex.org/W2804604520, https://openalex.org/W3003504112, https://openalex.org/W3125389312, https://openalex.org/W2785549520, https://openalex.org/W2250533720, https://openalex.org/W2963078493, https://openalex.org/W4297733535, https://openalex.org/W4200297453, https://openalex.org/W4280500719, https://openalex.org/W1983719983, https://openalex.org/W4297571622, https://openalex.org/W1959608418, https://openalex.org/W3186929568, https://openalex.org/W4200576357, https://openalex.org/W3034841376, https://openalex.org/W207220247, https://openalex.org/W2963271116, https://openalex.org/W4294562888, https://openalex.org/W3045464143, https://openalex.org/W3105398416 |
| referenced_works_count | 36 |
| abstract_inverted_index.1 | 81 |
| abstract_inverted_index.a | 19, 65, 74 |
| abstract_inverted_index.In | 118 |
| abstract_inverted_index.We | 41, 70, 84 |
| abstract_inverted_index.an | 44 |
| abstract_inverted_index.at | 142 |
| abstract_inverted_index.by | 35, 60 |
| abstract_inverted_index.in | 18 |
| abstract_inverted_index.is | 122, 140 |
| abstract_inverted_index.of | 4, 25, 79, 114 |
| abstract_inverted_index.on | 89, 103 |
| abstract_inverted_index.to | 14, 73, 125 |
| abstract_inverted_index.up | 11 |
| abstract_inverted_index.EHR | 30, 58, 76, 90, 116 |
| abstract_inverted_index.Our | 138 |
| abstract_inverted_index.The | 1 |
| abstract_inverted_index.and | 38, 92, 128, 135 |
| abstract_inverted_index.for | 132 |
| abstract_inverted_index.has | 32 |
| abstract_inverted_index.its | 36, 86 |
| abstract_inverted_index.our | 107, 120 |
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| abstract_inverted_index.also | 123 |
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| abstract_inverted_index.from | 28, 57, 64 |
| abstract_inverted_index.over | 80, 99 |
| abstract_inverted_index.way. | 21 |
| abstract_inverted_index.(EHR) | 8 |
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| abstract_inverted_index.human | 16 |
| abstract_inverted_index.model | 108, 121 |
| abstract_inverted_index.noisy | 39 |
| abstract_inverted_index.opens | 10 |
| abstract_inverted_index.rapid | 2 |
| abstract_inverted_index.topic | 50 |
| abstract_inverted_index.codes. | 117 |
| abstract_inverted_index.graph. | 69 |
| abstract_inverted_index.growth | 3 |
| abstract_inverted_index.health | 6 |
| abstract_inverted_index.latent | 54 |
| abstract_inverted_index.model. | 51 |
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| abstract_inverted_index.tasks. | 105 |
| abstract_inverted_index.topics | 56 |
| abstract_inverted_index.GAT-ETM | 52, 72, 95 |
| abstract_inverted_index.GitHub. | 144 |
| abstract_inverted_index.applied | 71 |
| abstract_inverted_index.dataset | 77 |
| abstract_inverted_index.disease | 55 |
| abstract_inverted_index.learned | 109 |
| abstract_inverted_index.medical | 67 |
| abstract_inverted_index.methods | 102 |
| abstract_inverted_index.million | 82 |
| abstract_inverted_index.patient | 130, 133 |
| abstract_inverted_index.present | 42 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.GAT-ETM, | 43 |
| abstract_inverted_index.However, | 22 |
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| abstract_inverted_index.datasets | 9 |
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| abstract_inverted_index.diseases | 17 |
| abstract_inverted_index.distills | 53 |
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| abstract_inverted_index.sparsity | 37 |
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| abstract_inverted_index.Moreover, | 106 |
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| abstract_inverted_index.knowledge | 27, 46, 68 |
| abstract_inverted_index.patients. | 83 |
| abstract_inverted_index.promising | 12 |
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| abstract_inverted_index.extraction | 24 |
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| abstract_inverted_index.systematic | 20 |
| abstract_inverted_index.understand | 15 |
| abstract_inverted_index.additional, | 119 |
| abstract_inverted_index.alternative | 101 |
| abstract_inverted_index.constructed | 66 |
| abstract_inverted_index.graph-based | 47 |
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| abstract_inverted_index.demonstrated | 96 |
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| abstract_inverted_index.reconstruction | 91 |
| abstract_inverted_index.stratification | 134 |
| abstract_inverted_index.representations | 131 |
| abstract_inverted_index.recommendations. | 137 |
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| corresponding_author_ids | https://openalex.org/A5100387861 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I5023651 |
| citation_normalized_percentile.value | 0.53376186 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |