Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Aug 17, 2023
Date Accepted: Dec 8, 2023
Predicting depression risk in patients with cancer using multimodal data
ABSTRACT
Background:
Patients with cancer starting systemic treatment programs, such as chemotherapy, often develop depression. A prediction model may assist physicians and healthcare workers in the early identification of these vulnerable patients.
Objective:
We developed a prediction model for depression risk over the course of cancer treatment.
Methods:
We included 16,159 patients diagnosed with cancer starting chemo- or radiotherapy treatment between 2008-2021. Machine learning (ML) and Natural Language Processing (NLP) techniques were used to develop multimodal prediction models using both EHR data and unstructured text (patient emails and clinician notes). Model performance was assessed in an independent test set (n=5,387, 33%) using area under the receiver operating characteristic curve (AUROC) and calibration curves.
Results:
Among 16,159 patients, 437 (3%) received a depression diagnosis within the first month of treatment. The LASSO logistic regression model based on the structured data had the best discriminative performance (0.74 AUROC, 95% CI 0.71-0.78), whereas a model based solely on clinician notes performed poorly (0.50 AUROC, 95% CI 0.49-0.52). Risks were underestimated for female and Black patients.
Conclusions:
After further validation, prediction models for depression risk could lead to earlier identification and treatment of vulnerable patients, ultimately improving cancer care and treatment adherence.
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.