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Continuous Data-Driven Monitoring in Critical Congenital Heart Disease: Clinical Deterioration Model Development.

Ruben S ZoodsmaRian BoschThomas AlderliestenCasper W BollenTeus H KappenErik KoomenArno SiebesJoppe Nijman
Published in: JMIR cardio (2023)
In this proof-of-concept study, a clinical deterioration detection algorithm was developed and retrospectively evaluated to classify clinical stability and instability, achieving reasonable performance considering the heterogeneous population of neonates with cCHD. Combined analysis of baseline (ie, patient-specific) deviations and simultaneous parameter-shifting (ie, population-specific) proofs would be promising with respect to enhancing applicability to heterogeneous critically ill pediatric populations. After prospective validation, the current-and comparable-models may, in the future, be used in the automated detection of clinical deterioration and eventually provide data-driven monitoring support to the medical team, allowing for timely intervention.
Keyphrases
  • congenital heart disease
  • deep learning
  • randomized controlled trial
  • healthcare
  • machine learning
  • palliative care
  • high throughput
  • quantum dots
  • label free
  • single cell