Domain-invariant Clinical Representation Learning by Bridging Data Distribution Shift across EMR Datasets Article Swipe
Zhongji Zhang
,
Yuhang Wang
,
Yinghao Zhu
,
Xinyu Ma
,
Tianlong Wang
,
Chaohe Zhang
,
Yasha Wang
,
Liantao Ma
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2310.07799
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2310.07799
Emerging diseases present challenges in symptom recognition and timely clinical intervention due to limited available information. An effective prognostic model could assist physicians in making accurate diagnoses and designing personalized treatment plans to prevent adverse outcomes. However, in the early stages of disease emergence, several factors hamper model development: limited data collection, insufficient clinical experience, and privacy and ethical concerns restrict data availability and complicate accurate label assignment. Furthermore, Electronic Medical Record (EMR) data from different diseases or sources often exhibit significant cross-dataset feature misalignment, severely impacting the effectiveness of deep learning models. We present a domain-invariant representation learning method that constructs a transition model between source and target datasets. By constraining the distribution shift of features generated across different domains, we capture domain-invariant features specifically relevant to downstream tasks, developing a unified domain-invariant encoder that achieves better feature representation across various task domains. Experimental results across multiple target tasks demonstrate that our proposed model surpasses competing baseline methods and achieves faster training convergence, particularly when working with limited data. Extensive experiments validate our method's effectiveness in providing more accurate predictions for emerging pandemics and other diseases. Code is publicly available at https://github.com/wang1yuhang/domain_invariant_network.
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Concepts
Computer science
Feature learning
Artificial intelligence
Machine learning
Transfer of learning
Deep learning
Invariant (physics)
Feature (linguistics)
Encoder
Representation (politics)
Domain (mathematical analysis)
Data mining
Mathematical physics
Politics
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Law
Philosophy
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Physics
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Political science
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2310.07799
- https://arxiv.org/pdf/2310.07799
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387634911
All OpenAlex metadata
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https://openalex.org/W4387634911Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2310.07799Digital Object Identifier
- Title
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Domain-invariant Clinical Representation Learning by Bridging Data Distribution Shift across EMR DatasetsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-10-11Full publication date if available
- Authors
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Zhongji Zhang, Yuhang Wang, Yinghao Zhu, Xinyu Ma, Tianlong Wang, Chaohe Zhang, Yasha Wang, Liantao MaList of authors in order
- Landing page
-
https://arxiv.org/abs/2310.07799Publisher landing page
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https://arxiv.org/pdf/2310.07799Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2310.07799Direct OA link when available
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Computer science, Feature learning, Artificial intelligence, Machine learning, Transfer of learning, Deep learning, Invariant (physics), Feature (linguistics), Encoder, Representation (politics), Domain (mathematical analysis), Data mining, Mathematical physics, Politics, Operating system, Law, Philosophy, Linguistics, Mathematics, Physics, Mathematical analysis, Political scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.tasks | 149 |
| abstract_inverted_index.Record | 71 |
| abstract_inverted_index.across | 118, 140, 146 |
| abstract_inverted_index.assist | 21 |
| abstract_inverted_index.better | 137 |
| abstract_inverted_index.faster | 161 |
| abstract_inverted_index.hamper | 46 |
| abstract_inverted_index.making | 24 |
| abstract_inverted_index.method | 99 |
| abstract_inverted_index.source | 106 |
| abstract_inverted_index.stages | 40 |
| abstract_inverted_index.target | 108, 148 |
| abstract_inverted_index.tasks, | 129 |
| abstract_inverted_index.timely | 8 |
| abstract_inverted_index.Medical | 70 |
| abstract_inverted_index.adverse | 34 |
| abstract_inverted_index.between | 105 |
| abstract_inverted_index.capture | 122 |
| abstract_inverted_index.disease | 42 |
| abstract_inverted_index.encoder | 134 |
| abstract_inverted_index.ethical | 58 |
| abstract_inverted_index.exhibit | 80 |
| abstract_inverted_index.factors | 45 |
| abstract_inverted_index.feature | 83, 138 |
| abstract_inverted_index.limited | 13, 49, 168 |
| abstract_inverted_index.methods | 158 |
| abstract_inverted_index.models. | 92 |
| abstract_inverted_index.present | 2, 94 |
| abstract_inverted_index.prevent | 33 |
| abstract_inverted_index.privacy | 56 |
| abstract_inverted_index.results | 145 |
| abstract_inverted_index.several | 44 |
| abstract_inverted_index.sources | 78 |
| abstract_inverted_index.symptom | 5 |
| abstract_inverted_index.unified | 132 |
| abstract_inverted_index.various | 141 |
| abstract_inverted_index.working | 166 |
| abstract_inverted_index.Emerging | 0 |
| abstract_inverted_index.However, | 36 |
| abstract_inverted_index.accurate | 25, 65, 179 |
| abstract_inverted_index.achieves | 136, 160 |
| abstract_inverted_index.baseline | 157 |
| abstract_inverted_index.clinical | 9, 53 |
| abstract_inverted_index.concerns | 59 |
| abstract_inverted_index.diseases | 1, 76 |
| abstract_inverted_index.domains, | 120 |
| abstract_inverted_index.domains. | 143 |
| abstract_inverted_index.emerging | 182 |
| abstract_inverted_index.features | 116, 124 |
| abstract_inverted_index.learning | 91, 98 |
| abstract_inverted_index.method's | 174 |
| abstract_inverted_index.multiple | 147 |
| abstract_inverted_index.proposed | 153 |
| abstract_inverted_index.publicly | 189 |
| abstract_inverted_index.relevant | 126 |
| abstract_inverted_index.restrict | 60 |
| abstract_inverted_index.severely | 85 |
| abstract_inverted_index.training | 162 |
| abstract_inverted_index.validate | 172 |
| abstract_inverted_index.Extensive | 170 |
| abstract_inverted_index.available | 14, 190 |
| abstract_inverted_index.competing | 156 |
| abstract_inverted_index.datasets. | 109 |
| abstract_inverted_index.designing | 28 |
| abstract_inverted_index.diagnoses | 26 |
| abstract_inverted_index.different | 75, 119 |
| abstract_inverted_index.diseases. | 186 |
| abstract_inverted_index.effective | 17 |
| abstract_inverted_index.generated | 117 |
| abstract_inverted_index.impacting | 86 |
| abstract_inverted_index.outcomes. | 35 |
| abstract_inverted_index.pandemics | 183 |
| abstract_inverted_index.providing | 177 |
| abstract_inverted_index.surpasses | 155 |
| abstract_inverted_index.treatment | 30 |
| abstract_inverted_index.Electronic | 69 |
| abstract_inverted_index.challenges | 3 |
| abstract_inverted_index.complicate | 64 |
| abstract_inverted_index.constructs | 101 |
| abstract_inverted_index.developing | 130 |
| abstract_inverted_index.downstream | 128 |
| abstract_inverted_index.emergence, | 43 |
| abstract_inverted_index.physicians | 22 |
| abstract_inverted_index.prognostic | 18 |
| abstract_inverted_index.transition | 103 |
| abstract_inverted_index.assignment. | 67 |
| abstract_inverted_index.collection, | 51 |
| abstract_inverted_index.demonstrate | 150 |
| abstract_inverted_index.experience, | 54 |
| abstract_inverted_index.experiments | 171 |
| abstract_inverted_index.predictions | 180 |
| abstract_inverted_index.recognition | 6 |
| abstract_inverted_index.significant | 81 |
| abstract_inverted_index.Experimental | 144 |
| abstract_inverted_index.Furthermore, | 68 |
| abstract_inverted_index.availability | 62 |
| abstract_inverted_index.constraining | 111 |
| abstract_inverted_index.convergence, | 163 |
| abstract_inverted_index.development: | 48 |
| abstract_inverted_index.distribution | 113 |
| abstract_inverted_index.information. | 15 |
| abstract_inverted_index.insufficient | 52 |
| abstract_inverted_index.intervention | 10 |
| abstract_inverted_index.particularly | 164 |
| abstract_inverted_index.personalized | 29 |
| abstract_inverted_index.specifically | 125 |
| abstract_inverted_index.cross-dataset | 82 |
| abstract_inverted_index.effectiveness | 88, 175 |
| abstract_inverted_index.misalignment, | 84 |
| abstract_inverted_index.representation | 97, 139 |
| abstract_inverted_index.domain-invariant | 96, 123, 133 |
| abstract_inverted_index.https://github.com/wang1yuhang/domain_invariant_network. | 192 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 8 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.75 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile |