Predicting Chronicity in Children and Adolescents With Newly Diagnosed Immune Thrombocytopenia at the Timepoint of Diagnosis Using Machine Learning‐Based Approaches

ML-based prediction models can identify children at increased risk for cITP at the time of diagnosis. Integration of such tools into prospective registries may enhance prognostic accuracy and support individualized clinical decision-making.

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Publication Details

Journal
Pediatric Blood & Cancer
Published
2026-01-12
DOI
https://doi.org/10.1002/1545-5017.70103
Primary Topic
Platelet Disorders and Treatments
Type
article
Field-Weighted Citation Impact
0.00
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article

Predicting Chronicity in Children and Adolescents With Newly Diagnosed Immune Thrombocytopenia at the Timepoint of Diagnosis Using Machine Learning‐Based Approaches

Alice Bizeul, Julia E. Vogt, Hugo Donato, T. Kühne et al.
Pediatric Blood & Cancer
Platelet Disorders and Treatments
article

Predicting Chronicity in Children and Adolescents With Newly Diagnosed Immune Thrombocytopenia at the Timepoint of Diagnosis Using Machine Learning‐Based Approaches

Alice Bizeul, Julia E. Vogt, Hugo Donato, T. Kühne, Severin Kasser, Meera Chitlur, Jelena Roganovic
article en

Abstract

ML-based prediction models can identify children at increased risk for cITP at the time of diagnosis. Integration of such tools into prospective registries may enhance prognostic accuracy and support individualized clinical decision-making.

Pediatric Blood & CancerVol. 73(3)
ETH Zurich (CH), Infant Foundation (AR), University Children’s Hospital Basel (CH), Children's Hospital of Michigan (US), Children's Hospital Zagreb (HR)
Peace, Justice and strong institutions
Openalex Percentile: Top 92%
Platelet Disorders and Treatments
DOIOpen Access PDFOpenAlex
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Predicting Chronicity in Children and Adolescents With Newly Diagnosed Immune Thrombocytopenia at the Timepoint of Diagnosis Using Machine Learning‐Based Approaches — Alice Bizeul, Julia E. Vogt, et al. · Pediatric Blood & Cancer (2026) | TGRS Research Map | TGRS