Learnable Prompt as Pseudo-Imputation: Rethinking the Necessity of Traditional EHR Data Imputation in Downstream Clinical Prediction Article Swipe
Weibin Liao
,
Yinghao Zhu
,
Zixiang Wang
,
Xu Chu
,
Yasha Wang
,
Liantao Ma
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2401.16796
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2401.16796
Analyzing the health status of patients based on Electronic Health Records (EHR) is a fundamental research problem in medical informatics. The presence of extensive missing values in EHR makes it challenging for deep neural networks (DNNs) to directly model the patient's health status. Existing DNNs training protocols, including Impute-then-Regress Procedure and Jointly Optimizing of Impute-n-Regress Procedure, require the additional imputation models to reconstruction missing values. However, Impute-then-Regress Procedure introduces the risk of injecting imputed, non-real data into downstream clinical prediction tasks, resulting in power loss, biased estimation, and poorly performing models, while Jointly Optimizing of Impute-n-Regress Procedure is also difficult to generalize due to the complex optimization space and demanding data requirements. Inspired by the recent advanced literature of learnable prompt in the fields of NLP and CV, in this work, we rethought the necessity of the imputation model in downstream clinical tasks, and proposed Learnable Prompt as Pseudo-Imputation (PAI) as a new training protocol to assist EHR analysis. PAI no longer introduces any imputed data but constructs a learnable prompt to model the implicit preferences of the downstream model for missing values, resulting in a significant performance improvement for all state-of-the-arts EHR analysis models on four real-world datasets across two clinical prediction tasks. Further experimental analysis indicates that PAI exhibits higher robustness in situations of data insufficiency and high missing rates. More importantly, as a plug-and-play protocol, PAI can be easily integrated into any existing or even imperceptible future EHR analysis models.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2401.16796
- https://arxiv.org/pdf/2401.16796
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391421181
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4391421181Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2401.16796Digital Object Identifier
- Title
-
Learnable Prompt as Pseudo-Imputation: Rethinking the Necessity of Traditional EHR Data Imputation in Downstream Clinical PredictionWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-01-30Full publication date if available
- Authors
-
Weibin Liao, Yinghao Zhu, Zixiang Wang, Xu Chu, Yasha Wang, Liantao MaList of authors in order
- Landing page
-
https://arxiv.org/abs/2401.16796Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2401.16796Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2401.16796Direct OA link when available
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Imputation (statistics), Downstream (manufacturing), Computer science, Data mining, Econometrics, Statistics, Missing data, Mathematics, Machine learning, Engineering, Operations managementTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.performance | 187 |
| abstract_inverted_index.preferences | 175 |
| abstract_inverted_index.significant | 186 |
| abstract_inverted_index.experimental | 205 |
| abstract_inverted_index.importantly, | 223 |
| abstract_inverted_index.informatics. | 19 |
| abstract_inverted_index.optimization | 106 |
| abstract_inverted_index.imperceptible | 238 |
| abstract_inverted_index.insufficiency | 217 |
| abstract_inverted_index.plug-and-play | 226 |
| abstract_inverted_index.requirements. | 111 |
| abstract_inverted_index.reconstruction | 62 |
| abstract_inverted_index.Impute-n-Regress | 54, 95 |
| abstract_inverted_index.Pseudo-Imputation | 148 |
| abstract_inverted_index.state-of-the-arts | 191 |
| abstract_inverted_index.Impute-then-Regress | 48, 66 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |