Post-treatment infection prediction in CLL using domain adaptation of lymphoma electronic health records.

ABSTRACT:

Background and purpose

Infections are the leading cause of morbidity and mortality in patients with chronic lymphocytic leukemia (CLL) and occur during and after treatment. When deciding on the type of CLL treatment, the risk of infections is typically assessed based only on age and comorbidities; therefore, there is a need to develop a predictive model that incorporates information from multiple data sources. However, training an effective machine learning model requires a large sample size. Patient/material and methods: In this study, we developed a machine learning approach using domain adaptation (DA) to predict the risk of severe infection during treatment in patients with CLL. We implemented a DA strategy using lymphoma patient data and compared it with a domain-specific (DS)

SUBMITTER: Parviz M 

PROVIDER: S-EPMC12927150 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature