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Updated: May 24, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

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Published on: July 22, 2025

Test-Time Data Quality Degradation in Clinical ML: A Systematic Robustness Analysis on MIMIC-IV.

Nour Idris Pacha1,2,3, Saber Aloui1, Asma Rabaoui2

  • 1Angers University Hospital, Angers, France.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary

Clinical classification models show reduced performance when patient data quality degrades. This impacts the reliability of machine learning predictions for in-hospital mortality, highlighting the need for robust data handling in healthcare.

Keywords:
Data qualityMIMIC-IVclinical machine learningelectronic health recordsrobustnesstest-time degradation

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Last Updated: May 24, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

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Published on: July 22, 2025

Area of Science:

  • Clinical informatics
  • Machine learning in healthcare
  • Data quality assessment

Background:

  • Machine learning models are increasingly used for clinical predictions.
  • The reliability of these models depends on the quality of input data.
  • Data quality degradation can occur in real-world clinical settings.

Purpose of the Study:

  • To evaluate the robustness of clinical classification models against data quality degradation.
  • To assess the impact of different types of data corruption on model performance.

Main Methods:

  • A controlled experimental study was performed using the MIMIC-IV database.
  • Five clinical classification models were trained on clean data.
  • Dimension-specific data corruptions were applied at test time to simulate data quality degradation.

Main Results:

  • Model performance, measured by AUROC and Brier score, declined as data quality degradation increased.
  • Completeness, coherence, and validity degradations had a stronger negative impact than precision degradation.
  • The reliability of machine learning-based predictions for in-hospital mortality was substantially compromised.

Conclusions:

  • Data quality degradation significantly impacts the performance of clinical classification models.
  • Ensuring high data quality is crucial for reliable machine learning predictions in healthcare.
  • Further research is needed to develop methods for improving model robustness to data quality issues.