MedDiff: Generating Electronic Health Records using Accelerated Denoising Diffusion Model Article Swipe
Huan He
,
Shifan Zhao
,
Yuanzhe Xi
,
Joyce C. Ho
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2302.04355
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2302.04355
Due to patient privacy protection concerns, machine learning research in healthcare has been undeniably slower and limited than in other application domains. High-quality, realistic, synthetic electronic health records (EHRs) can be leveraged to accelerate methodological developments for research purposes while mitigating privacy concerns associated with data sharing. The current state-of-the-art model for synthetic EHR generation is generative adversarial networks, which are notoriously difficult to train and can suffer from mode collapse. Denoising Diffusion Probabilistic Models, a class of generative models inspired by statistical thermodynamics, have recently been shown to generate high-quality synthetic samples in certain domains. It is unknown whether these can generalize to generation of large-scale, high-dimensional EHRs. In this paper, we present a novel generative model based on diffusion models that is the first successful application on electronic health records. Our model proposes a mechanism to perform class-conditional sampling to preserve label information. We also introduce a new sampling strategy to accelerate the inference speed. We empirically show that our model outperforms existing state-of-the-art synthetic EHR generation methods.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2302.04355
- https://arxiv.org/pdf/2302.04355
- OA Status
- green
- Cited By
- 9
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4320342418
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4320342418Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2302.04355Digital Object Identifier
- Title
-
MedDiff: Generating Electronic Health Records using Accelerated Denoising Diffusion ModelWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-02-08Full publication date if available
- Authors
-
Huan He, Shifan Zhao, Yuanzhe Xi, Joyce C. HoList of authors in order
- Landing page
-
https://arxiv.org/abs/2302.04355Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2302.04355Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2302.04355Direct OA link when available
- Concepts
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Computer science, Inference, Class (philosophy), Generative grammar, Probabilistic logic, Generative model, Health records, Artificial intelligence, Machine learning, Data mining, Quality (philosophy), Data science, Health care, Philosophy, Economics, Epistemology, Economic growthTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
9Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 4, 2024: 1, 2023: 4Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.networks, | 58 |
| abstract_inverted_index.synthetic | 24, 52, 91, 166 |
| abstract_inverted_index.accelerate | 33, 153 |
| abstract_inverted_index.associated | 43 |
| abstract_inverted_index.electronic | 25, 129 |
| abstract_inverted_index.generalize | 102 |
| abstract_inverted_index.generation | 54, 104, 168 |
| abstract_inverted_index.generative | 56, 78, 116 |
| abstract_inverted_index.healthcare | 10 |
| abstract_inverted_index.mitigating | 40 |
| abstract_inverted_index.protection | 4 |
| abstract_inverted_index.realistic, | 23 |
| abstract_inverted_index.successful | 126 |
| abstract_inverted_index.undeniably | 13 |
| abstract_inverted_index.adversarial | 57 |
| abstract_inverted_index.application | 20, 127 |
| abstract_inverted_index.empirically | 158 |
| abstract_inverted_index.notoriously | 61 |
| abstract_inverted_index.outperforms | 163 |
| abstract_inverted_index.statistical | 82 |
| abstract_inverted_index.developments | 35 |
| abstract_inverted_index.high-quality | 90 |
| abstract_inverted_index.information. | 144 |
| abstract_inverted_index.large-scale, | 106 |
| abstract_inverted_index.High-quality, | 22 |
| abstract_inverted_index.Probabilistic | 73 |
| abstract_inverted_index.methodological | 34 |
| abstract_inverted_index.thermodynamics, | 83 |
| abstract_inverted_index.high-dimensional | 107 |
| abstract_inverted_index.state-of-the-art | 49, 165 |
| abstract_inverted_index.class-conditional | 139 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile |