Using real-world electronic health record data to predict the development of 12 cancer-related symptoms in the context of multimorbidity.

ABSTRACT:

Objective

This study uses electronic health record (EHR) data to predict 12 common cancer symptoms, assessing the efficacy of machine learning (ML) models in identifying symptom influencers.

Materials and methods

We analyzed EHR data of 8156 adults diagnosed with cancer who underwent cancer treatment from 2017 to 2020. Structured and unstructured EHR data were sourced from the Enterprise Data Warehouse for Research at the University of Iowa Hospital and Clinics. Several predictive models, including logistic regression, random forest (RF), and XGBoost, were employed to forecast symptom development. The performances of the models were evaluated by F1-score and area under the curve (AUC) on the testing set. The SHapley Additive exPlanations framework was used to interpret these

SUBMITTER: Bandyopadhyay A 

PROVIDER: S-EPMC11397936 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature