Using Machine Learning to Predict Unplanned Hospital Utilization and Chemotherapy Management From Patient-Reported Outcome Measures.

Journal: JCO clinical cancer informatics

Published Date: Apr 1, 2024

Abstract

PURPOSE: Adverse effects of chemotherapy often require hospital admissions or treatment management. Identifying factors contributing to unplanned hospital utilization may improve health care quality and patients' well-being. This study aimed to assess if patient-reported outcome measures (PROMs) improve performance of machine learning (ML) models predicting hospital admissions, triage events (contacting helpline or attending hospital), and changes to chemotherapy.

Authors

Zuzanna Wójcik

UKRI Centre for Doctoral Training in Artificial Intelligence for Medical Diagnosis and Care, University of Leeds, Leeds, United Kingdom.
Vania Dimitrova

School of Computing, University of Leeds, Leeds, United Kingdom.
Lorraine Warrington

Leeds Institute of Medical Research, University of Leeds, St James's University Hospital, Leeds, United Kingdom.
Galina Velikova

Leeds Institute of Medical Research, University of Leeds, St James's University Hospital, Leeds, United Kingdom.
Kate Absolom

Leeds Institute of Medical Research, University of Leeds, St James's University Hospital, Leeds, United Kingdom.

Keywords

Adult Aged Antineoplastic Agents Female Hospitalization Humans Machine Learning Male Middle Aged Neoplasms Patient Reported Outcome Measures Quality of Life Surveys and Questionnaires

External Resources

View on PubMed Access via DOI PubMed (38669610)

Using Machine Learning to Predict Unplanned Hospital Utilization and Chemotherapy Management From Patient-Reported Outcome Measures.

Abstract

Authors

Keywords

External Resources

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