Author Correction: A multimodal embedding model for sepsis data representation - Report - MDSpire
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Correction Notice: A Comprehensive Embedding Framework for Representing Sepsis Data

  • By

  • Tuo Liu

  • Yonglin Li

  • Hongyi Chen

  • Naiqing Li

  • Yan Zhang

  • Xuanqi Huang

  • Jin Wang

  • Rui Chen

  • Yuping Zeng

  • Yuntao Liu

  • Danwen Zheng

  • Darong Wu

  • Changdong Wang

  • Tao Yu

  • Xiaotu Xi

  • Zhongde Zhang

  • August 13, 2026

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Correction Notice: A Comprehensive Embedding Framework for Representing Sepsis Data

Overview

This correction notice addresses discrepancies in the original publication regarding the sequence of variables and inaccuracies in unit labels and variable names.

Background

Sepsis is a critical condition that results from an abnormal response to infection, leading to significant morbidity and mortality. Accurate data representation is essential for effective clinical decision-making and research in sepsis management.

Data Highlights

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

Key Findings

  • The original article had a mismatch between the sequence of variables in the statistical output and their arrangement in Tables 1 and 2.
  • Inaccuracies were found in various unit labels and variable names.
  • The Sepsis Data Representation Model (SepsisDRM) aims to process both tabular and textual information for better patient representation.

Clinical Implications

The corrections made in this notice are vital for maintaining the reliability of findings in future studies.

Conclusion

This correction notice details the necessary adjustments to ensure accurate data representation.

Related Resources & Content

  1. npj Digital Medicine, 2026 -- Correction to: A Comprehensive Embedding Framework for Representing Sepsis Data
  2. npj Digital Medicine — A multimodal embedding model for sepsis data representation
  3. JMIR Medical Informatics — Online Sepsis Prediction Using Vital Signs and Multiscale Temporal-Aware Contrastive Learning: Model Development and Validation Study
  4. Intensive Care Medicine — The next frontier in sepsis: connected ICU data for real-world clinical decision making
  5. BMJ Health & Care Informatics — Early sepsis prediction using a hybrid LSTM-GAT model: a study on the PhysioNet 2019 dataset
  6. A multimodal embedding model for sepsis data representation
  7. Online Sepsis Prediction Using Vital Signs and Multiscale Temporal-Aware Contrastive Learning: Model Development and Validation Study
  8. The next frontier in sepsis: connected ICU data for real-world clinical decision making
  9. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3)
  10. Liberal Versus Restrictive Fluid Therapy in the Early Management of Sepsis and Septic Shock: A Systematic Review - PubMed
  11. Evidence Maps of Vasopressor Use in Adult Patients With Septic Shock: An Umbrella Review - PubMed
  12. What are the benefits and harms of corticosteroids in the treatment of children and adults with sepsis? | Cochrane
  13. Predictive value of SOFA, PCT, Lactate, qSOFA and their combinations for mortality in patients with sepsis: A systematic review and meta-analysis - PMC

Original Source(s)

Author Correction: A multimodal embedding model for sepsis data representation

  • by Tuo Liu, Yonglin Li, Hongyi Chen, Naiqing Li, Yan Zhang, Xuanqi Huang, Jin Wang, Rui Chen, Yuping Zeng, Yuntao Liu, Danwen Zheng, Darong Wu, Changdong Wang, Tao Yu, Xiaotu Xi, Zhongde Zhang

  • August 13, 2026

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