Machine Learning for Prediction of High-Risk Hospitalizations in Lymphoma Patients: A Danish Population-Based Study.

Journal: European journal of haematology
Published Date: Jul 29, 2025

Abstract

OBJECTIVE: Infections are a leading cause of hospitalization in patients treated for lymphoma and can be life-threatening. This study developed a machine learning (ML)-based risk stratification method to classify infection-related hospitalizations (IRH) into serious-IRH (S-IRH) and non-serious-IRH (NS-IRH).

Authors

  • Alexander Djupnes Fuglkjær
    Operations Research Group, Department of Materials and Production, Aalborg University, Aalborg, 9220, Denmark.
  • Deniz Kenan Kilic
    Department of Materials and Production, Aalborg University, Aalborg, Denmark.
  • Mathias Holmsgaard Eskesen
    Department of Oncology, Clinical Cancer Research Center, Aalborg University Hospital, Aalborg, Denmark.
  • Mikkel Runason Simonsen
    Department of Hematology, Clinical Cancer Research Center, Aalborg University Hospital, Aalborg, Denmark.
  • Laurids Østergaard Poulsen
    Department of Oncology, Clinical Cancer Research Center, Aalborg University Hospital, Aalborg, Denmark.
  • Carsten Utoft Niemann
    Department of Hematology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark.
  • Paw Jensen
    Department of Clinical Medicine, Aalborg University, Aalborg, Denmark.
  • Kirstine Kobberøe Søgaard
    Department of Clinical Medicine, Aalborg University, Aalborg, Denmark.
  • Mikkel Werling
    Copenhagen University Hospital, Rigshospitalet, Department of Hematology, Copenhagen, Denmark.
  • Frederik Christensen
    Operations Research Group, Department of Materials and Production, Aalborg University, Aalborg, 9220, Denmark. Electronic address: frch@mp.aau.dk.
  • Izabela Ewa Nielsen
    Operations Research Group, Department of Materials and Production, Aalborg University, Aalborg, 9220, Denmark.
  • Tarec Christoffer El-Galaly
    Department of Haematology, Aalborg University Hospital, Aalborg, 9000, Denmark.

Keywords

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