Evidence map›Paper›PMID 41174551›Full record

ArticleBMC cancer2025

Development and prospective evaluation of a machine learning model to predict vomiting among pediatric cancer and hematopoietic cell transplant patients.

Adam Paul Yan, Lin Lawrence Guo, Priya Patel, Tal Schechter, Santiago Eduardo Arciniegas, Jiro Inoue, Emily Vettese, Karim Jessa, Bren Cardiff, George A Tomlinson and 2 more

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In one paragraph

Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

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No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

12 authors.

Adam Paul Yan *Division of Haematology/Oncology, The Hospital for Sick Children, 555 University Avenue, Toronto, ON, M5G1X8, Canada.
Lin Lawrence Guo *Program in Child Health Evaluative Sciences, The Hospital for Sick Children, Peter Gilgan Centre for Research and Learning, Toronto, Canada.
Priya PatelDivision of Haematology/Oncology, The Hospital for Sick Children, 555 University Avenue, Toronto, ON, M5G1X8, Canada.
Tal SchechterDivision of Haematology/Oncology, The Hospital for Sick Children, 555 University Avenue, Toronto, ON, M5G1X8, Canada.
Santiago Eduardo ArciniegasProgram in Child Health Evaluative Sciences, The Hospital for Sick Children, Peter Gilgan Centre for Research and Learning, Toronto, Canada.
Jiro InoueProgram in Child Health Evaluative Sciences, The Hospital for Sick Children, Peter Gilgan Centre for Research and Learning, Toronto, Canada.
Emily VetteseProgram in Child Health Evaluative Sciences, The Hospital for Sick Children, Peter Gilgan Centre for Research and Learning, Toronto, Canada.
Karim JessaInformation Management Technology, The Hospital for Sick Children, Toronto, Canada.
Bren CardiffInformation Management Technology, The Hospital for Sick Children, Toronto, Canada.
George A TomlinsonDepartment of Medicine, Toronto General Hospital, Toronto, Canada.
L Lee DupuisProgram in Child Health Evaluative Sciences, The Hospital for Sick Children, Peter Gilgan Centre for Research and Learning, Toronto, Canada.
Lillian SungDivision of Haematology/Oncology, The Hospital for Sick Children, 555 University Avenue, Toronto, ON, M5G1X8, Canada. lillian.sung@sickkids.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeObjectives were to develop a machine learning (ML) model based on electronic health record (EHR) data to predict the risk of vomiting within a 96-hour window after admission to the pediatric oncology and hematopoietic cell transplant (HCT) services using retrospective data and to evaluate the model prospectively in a silent trial. PATIENTS AND

methodsAdmissions between 2018-06-02 to 2024-02-17 (retrospective) and 2024-05-09 to 2024-08-05 (prospective) to the oncology or HCT services were included. Data source was SEDAR, a curated and validated approach to deliver EHR data for ML. Prediction time was 08:30 the morning following admission. The outcome was any vomiting within 96 h following prediction time. We trained models using L2-regularized logistic regression, LightGBM and XGBoost. Training cohorts include the target cohort and all inpatient admissions.

resultsThere were 7,408 admissions in the retrospective phase and 340 admissions in the prospective silent trial phase. The best-performing model in the retrospective phase was the LightGBM model trained on all inpatients. The number of features in the final model was 2,859. The area-under-the-receiver-operating-characteristic curve (AUROC) was 0.730 (95% confidence interval (CI) 0.694-0.765) for the retrospective phase and 0.716 (95% CI 0.649-0.784) for the prospective silent trial phase.

conclusionsWe found that data in the EHR could be used to develop a retrospective ML model to predict vomiting among pediatric oncology and HCT inpatients. This model retained satisfactory performance in a prospective silent trial. Future plans will include deployment into clinical workflows and determining if the model improves vomiting control.

Indexed as

Hematopoietic Stem Cell TransplantationMachine LearningNeoplasmsVomitingAdolescentChildChild, PreschoolElectronic Health RecordsFemaleHumansInfantMaleProspective StudiesRetrospective StudiesEmesisHematopoietic cell transplantMachine learningOncologyProspective evaluation

Identifiers

PMID41174551
PMCPMC12577177

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.