Model stability of COVID-19 mortality prediction with biomarkers

Chenyan Huang, Xi Long (Corresponding author), Zhuozhao Zhan, Edwin R. van den Heuvel

Research output: Contribution to journalArticleAcademic

27 Downloads (Pure)


Coronavirus disease 2019 (COVID-19) is an unprecedented and fast evolving pandemic, which has caused a large number of critically ill patients and deaths globally. It is an acute public health crisis leading to overloaded critical care capacity. Timely prediction of the clinical outcome (death/survival) of hospital-admitted COVID-19 patients can provide early warnings to clinicians, allowing improved allocation of medical resources. In a recently published paper, an interpretable machine learning model was presented to predict the mortality of COVID-19 patients with blood biomarkers, where the model was trained and tested on relatively small data sets. However, the model or performance stability was not explored and assessed. By re-analyzing the data, we reveal that the reported mortality prediction performance was likely over-optimistic and its uncertainty was underestimated or overlooked, with a large variability in predicting deaths.
Original languageEnglish
Article number20161323
Number of pages8
Publication statusPublished - 30 Jul 2020


  • COVID-19
  • Mortality prediction
  • Biomarker
  • Model stability


Dive into the research topics of 'Model stability of COVID-19 mortality prediction with biomarkers'. Together they form a unique fingerprint.

Cite this