Creation and prospective assessment of a machine learning model for predicting vomiting in children undergoing cancer treatment and hematopoietic cell transplantation - Scorecard - MDSpire
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Creation and prospective assessment of a machine learning model for predicting vomiting in children undergoing cancer treatment and hematopoietic cell transplantation

  • By

  • Adam Paul Yan

  • Lin Lawrence Guo

  • Priya Patel

  • Tal Schechter

  • Santiago Eduardo Arciniegas

  • Jiro Inoue

  • Emily Vettese

  • Karim Jessa

  • Bren Cardiff

  • George A. Tomlinson

  • L. Lee Dupuis

  • Lillian Sung

  • October 31, 2025

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Clinical Scorecard: Creation and prospective assessment of a machine learning model for predicting vomiting in children undergoing cancer treatment and hematopoietic cell transplantation

At a Glance

CategoryDetail
ConditionVomiting in pediatric cancer and hematopoietic cell transplant patients, significantly impacting quality of life and healthcare costs.
Key Mechanisms
Target Population
Care Setting

Key Highlights

  • Study includes both retrospective model development and a prospective silent trial for evaluation, crucial for assessing model integration.

Guideline-Based Recommendations

Diagnosis

    Management

    • Implement machine learning predictions to guide anti-emetic therapy in pediatric oncology patients, ensuring personalized treatment plans.

    Monitoring & Follow-up

      Risks

        Patient & Prescribing Data

        Children undergoing cancer treatment or hematopoietic cell transplantation

        Focus on minimizing vomiting to improve nutritional status and reduce hospitalization

        Clinical Best Practices

        • Integrate machine learning models into clinical workflows for real-time vomiting risk assessment, while addressing potential biases.

        Related Resources & Content

        Original Source(s)

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