Correction: External validation of a machine learning model to predict hemodynamic instability in intensive care unit - Report - MDSpire
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Correction: Validation of a Machine Learning Approach for Predicting Hemodynamic Instability in the ICU

  • By

  • Chiang Dung‑Hung

  • Tian Cong

  • Jiang Zeyu

  • Ou‑Yang Yu‑Shan

  • Lin Yung‑Yang

  • September 29, 2026

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Clinical Report: Correction on Machine Learning for Hemodynamic Instability

Background

Machine learning has emerged as a significant tool in the intensive care setting, particularly for predicting outcomes such as hemodynamic instability. This correction highlights the importance of precise author attribution in scientific literature.

Data Highlights

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

Key Findings

  • Correction made to the authorship of the original article.
  • Lin Yung-Yang is the accurate author designation.
  • The correction follows the authors' update regarding the discrepancy.

Clinical Implications

Clinicians should be aware of the importance of accurate authorship in research publications, as it affects the credibility of the findings. Continued advancements in machine learning can support better patient outcomes in critical care settings.

Conclusion

This correction reinforces the need for accuracy in scientific authorship, particularly in the context of machine learning applications in critical care.

Related Resources & Content

  1. Lin Yung-Yang, Critical Care, 2022 -- Correction: Validation of a Machine Learning Approach for Predicting Hemodynamic Instability in the ICU
  2. DIGITAL HEALTH — Incremental domain adaptation-based ICU patient mortality prediction
  3. DIGITAL HEALTH — Predicting blood transfusion after ICU admission in five databases: A comparison of three machine learning paradigms
  4. Frontiers in Neurology — Development and internal validation of a machine learning model for predicting intracranial infection after spontaneous intracerebral hemorrhage: a two-center retrospective study
  5. DIGITAL HEALTH — Online explainable machine learning prediction of sepsis in hemorrhagic stroke: Development and multicenter external validation
  6. Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock 2026 | SCCM
  7. ESICM guidelines on circulatory shock and hemodynamic monitoring 2025 - PubMed
  8. New Publication | Clinical criteria for the definition of refractory septic shock: a joint Delphi consensus from the Society of Critical Care Medicine (SCCM) and European Society of Intensive Care Medicine (ESICM) - ESICM
  9. Personalized Hemodynamic Resuscitation Targeting Capillary Refill Time in Early Septic Shock: The ANDROMEDA-SHOCK-2 Randomized Clinical Trial | Acid Base, Electrolytes, Fluids | JAMA | JAMA Network
  10. Artificial intelligence tools in sepsis prediction: a systematic review and meta-analysis | npj Digital Medicine
  11. Frontiers | Prediction models for mortality in patients with sepsis: a systematic review and meta-analysis
  12. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods | The BMJ

Original Source(s)

Correction: External validation of a machine learning model to predict hemodynamic instability in intensive care unit

  • by Chiang Dung‑Hung, Tian Cong, Jiang Zeyu, Ou‑Yang Yu‑Shan, Lin Yung‑Yang

  • September 29, 2026

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