Summary of Learnable Prompt As Pseudo-imputation: Rethinking the Necessity Of Traditional Ehr Data Imputation in Downstream Clinical Prediction, by Weibin Liao et al.
Learnable Prompt as Pseudo-Imputation: Rethinking the Necessity of Traditional EHR Data Imputation in Downstream Clinical Prediction
by Weibin Liao, Yinghao Zhu, Zhongji Zhang, Yuhang Wang, Zixiang Wang, Xu Chu, Yasha Wang, Liantao Ma
First submitted to arxiv on: 30 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Learnable Prompt as Pseudo-Imputation (PAI) training protocol is designed to improve the performance of deep neural networks (DNNs) in analyzing Electronic Health Records (EHRs). The protocol eliminates the need for additional imputation models, which can introduce biased estimation and power loss. Instead, PAI constructs a learnable prompt that models the implicit preferences of the downstream model for missing values. This approach significantly improves performance on four real-world datasets across two clinical prediction tasks, while also exhibiting higher robustness in situations with data insufficiency or high missing rates. PAI can be easily integrated into existing or future EHR analysis models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary EHRs help doctors and researchers understand patients’ health better. But sometimes there are gaps in the records that make it hard for computers to learn from them. Scientists have tried different ways to fill these gaps, but some methods can actually make things worse. This paper introduces a new approach called Learnable Prompt as Pseudo-Imputation (PAI). Instead of filling in the blanks, PAI teaches computers how to understand what’s missing and use that information to make better predictions. This method works really well on real-world data and can be used with any computer program designed to analyze EHRs. |
Keywords
* Artificial intelligence * Prompt
