Clinical Report: Correction on Electronic Health Records for Patient Stratification

Overview

This report addresses the correction made to the original article regarding the utilization of electronic health records for transformer patient embedding. The funding information was omitted in the initial publication, which has now been rectified.

Background

The use of electronic health records (EHRs) in patient stratification and progression assessment is crucial. The integration of advanced computational methods, such as transformer models, into EHR analysis represents a significant advancement in this field.

Data Highlights

No numerical or trial data is presented in the correction notice.

Key Findings

• The original article was published on August 14, 2025, in npj Digital Medicine.
• The correction pertains to the omission of funding information from the original publication.
• The funding was provided by the National Institute of General Medical Sciences of the NIH under Award Number P20GM152305.
• The content of the article reflects the authors' views and does not necessarily represent the official views of the NIH.
• The correction was published online on June 11, 2026.

Clinical Implications

Healthcare professionals should be aware of the importance of accurate funding disclosures in research publications. This correction highlights the need for transparency in research support, which can impact the interpretation of study findings.

Conclusion

The correction to the original article emphasizes the importance of complete and accurate reporting in scientific literature, particularly regarding funding sources.

Related Resources & Content

1. Xian, S., Grabowska, M.E., Kullo, I.J. et al., npj Digital Medicine, 2026 -- Correction: Utilizing Electronic Health Records for Transformer Patient Embedding
2. npj Digital Medicine — Enhanced Transferability of Predictions from Electronic Health Records Across Different Countries and Coding Frameworks Using Large Language Models
3. aace endocrine ai — Selective LLM use may improve electronic health record phenotyping accuracy 
4. npj Digital Medicine — Bridging clinical knowledge and AI: an interpretable transformer framework for ECG diagnosis
5. Journal of Medical Internet Research (JMIR) — Automated Identification of Nursing Diagnoses and Interventions From Nursing Records Using a Retrieval-Augmented Large Language Model Approach: Quantitative Study
6. Enhanced Transferability of Predictions from Electronic Health Records
7. Selective LLM use may improve electronic health record phenotyping accuracy
8. Bridging clinical knowledge and AI: an interpretable transformer framework for ECG diagnosis
9. Marketing Submission Recommendations for a Predetermined Change Control Plan for AI-Enabled Device Software Functions | FDA
10. Transformer patient embedding using electronic health records enables patient stratification and progression analysis | npj Digital Medicine
11. Artificial Intelligence in Oncology: Clinical Applications, Challenges, and Opportunities | American Society of Clinical Oncology Educational Book