Imputation of Unknown Missingness in Sparse Electronic Health Records
arXiv:2602.20442v1 Announce Type: new Abstract: Machine learning holds great promise for advancing the field of medicine, with electronic health records (EHRs) serving as a primary data source. However, EHRs are often sparse and contain missing data due to various challenges and limitations in data collection and sharing between healthcare providers. Existing techniques for imputing missing values predominantly focus on known unknowns, such as missing or unavailable values of lab test results; most do not explicitly address situations where it is difficult to distinguish what is missing. For instance, a missing diagnosis code in an EHR could signify either that the patient has not been diagnosed with the condition or that a diagnosis was made, but not shared by a provider. Such situations fall into the paradigm of unknown unknowns. To address this challenge, we develop a general purpose algorithm for denoising data to recover unknown missing values in binary EHRs. We design a transfor
arXiv:2602.20442v1 Announce Type: new Abstract: Machine learning holds great promise for advancing the field of medicine, with electronic health records (EHRs) serving as a primary data source. However, EHRs are often sparse and contain missing data due to various challenges and limitations in data collection and sharing between healthcare providers. Existing techniques for imputing missing values predominantly focus on known unknowns, such as missing or unavailable values of lab test results; most do not explicitly address situations where it is difficult to distinguish what is missing. For instance, a missing diagnosis code in an EHR could signify either that the patient has not been diagnosed with the condition or that a diagnosis was made, but not shared by a provider. Such situations fall into the paradigm of unknown unknowns. To address this challenge, we develop a general purpose algorithm for denoising data to recover unknown missing values in binary EHRs. We design a transformer-based denoising neural network where the output is thresholded adaptively to recover values in cases where we predict data are missing. Our results demonstrate improved accuracy in denoising medical codes within a real EHR dataset compared to existing imputation approaches and leads to increased performance on downstream tasks using the denoised data. In particular, when applying our method to a real world application, predicting hospital readmission from EHRs, our method achieves statistically significant improvement over all existing baselines.
Executive Summary
This article presents a novel algorithm for imputing unknown missing values in sparse electronic health records (EHRs). The proposed method, a transformer-based denoising neural network, addresses the challenge of distinguishing between missing and absent data, a common issue in EHRs. The authors demonstrate improved accuracy in denoising medical codes and increased performance on downstream tasks, including predicting hospital readmission. This breakthrough has significant implications for the application of machine learning in medicine, particularly in the analysis of complex and incomplete EHRs. The method's adaptability and ability to handle unknown missing values make it a valuable tool for researchers and clinicians seeking to extract meaningful insights from EHRs.
Key Points
- ▸ The article proposes a transformer-based denoising neural network for imputing unknown missing values in EHRs.
- ▸ The method addresses the challenge of distinguishing between missing and absent data in EHRs.
- ▸ The authors demonstrate improved accuracy in denoising medical codes and increased performance on downstream tasks.
Merits
Advances in EHR Analysis
The proposed method enables more accurate analysis of EHRs, which is essential for extracting meaningful insights from complex and incomplete data.
Improved Downstream Performance
The authors demonstrate improved performance on downstream tasks, including predicting hospital readmission, which has significant implications for patient care and outcomes.
Demerits
Limited Generalizability
The method is developed and tested on a specific EHR dataset, and its generalizability to other datasets and domains is unclear.
Computational Complexity
The proposed method involves the use of a transformer-based neural network, which may be computationally expensive and require significant resources to implement.
Expert Commentary
The article presents a significant breakthrough in the analysis of EHRs, addressing a long-standing challenge in machine learning applications. The proposed method demonstrates improved accuracy and performance on downstream tasks, which is essential for extracting meaningful insights from complex and incomplete data. However, the method's limited generalizability and computational complexity are notable limitations that require further investigation. The article's implications for healthcare policy and practice are substantial, particularly in the development of evidence-based guidelines and best practices for EHR analysis and machine learning applications.
Recommendations
- ✓ Further investigation into the method's generalizability to other datasets and domains is necessary to ensure its broad applicability.
- ✓ The authors should consider implementing the method on larger and more diverse EHR datasets to evaluate its scalability and performance.
