Assessment of the Prevalence of Infections in Pediatric Patients With Acute Lymphoblastic Leukemia

Nair, Arun; Elballushi, Ruaa; Joshi, Riecha; Anjanappa, Sanvithi; Akter, Maksuda; Arif, Sehrish; Rehman, Sana

doi:10.7759/cureus.46837

Cited by 1 publication

(2 citation statements)

References 34 publications

(41 reference statements)

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“…Notably, there is no specific infection prediction model baseline from which to compare the performance of the presented regression model(s). Moreover, in these rare pediatric cancers, a quantitative standard infection risk protocol is not presently available as of the time of writing [ 60 ]. Most clinicians use neutropenia as the primary feature by which to predict infection [ 60 ].…”

Section: Resultsmentioning

confidence: 99%

“…Moreover, in these rare pediatric cancers, a quantitative standard infection risk protocol is not presently available as of the time of writing [ 60 ]. Most clinicians use neutropenia as the primary feature by which to predict infection [ 60 ]. Thus, in short, the presented model outperforms general expectations based on the wide variety of features anecdotally considered by a clinician.…”

Section: Resultsmentioning

confidence: 99%

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An Interpretable Machine Learning Framework for Rare Disease: A Case Study to Stratify Infection Risk in Pediatric Leukemia

Al-Hussaini,

White,

Varmeziar

et al. 2024

JCM

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Background: Datasets on rare diseases, like pediatric acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL), have small sample sizes that hinder machine learning (ML). The objective was to develop an interpretable ML framework to elucidate actionable insights from small tabular rare disease datasets. Methods: The comprehensive framework employed optimized data imputation and sampling, supervised and unsupervised learning, and literature-based discovery (LBD). The framework was deployed to assess treatment-related infection in pediatric AML and ALL. Results: An interpretable decision tree classified the risk of infection as either “high risk” or “low risk” in pediatric ALL (n = 580) and AML (n = 132) with accuracy of ∼79%. Interpretable regression models predicted the discrete number of developed infections with a mean absolute error (MAE) of 2.26 for bacterial infections and an MAE of 1.29 for viral infections. Features that best explained the development of infection were the chemotherapy regimen, cancer cells in the central nervous system at initial diagnosis, chemotherapy course, leukemia type, Down syndrome, race, and National Cancer Institute risk classification. Finally, SemNet 2.0, an open-source LBD software that links relationships from 33+ million PubMed articles, identified additional features for the prediction of infection, like glucose, iron, neutropenia-reducing growth factors, and systemic lupus erythematosus (SLE). Conclusions: The developed ML framework enabled state-of-the-art, interpretable predictions using rare disease tabular datasets. ML model performance baselines were successfully produced to predict infection in pediatric AML and ALL.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%