A computational framework for longitudinal medication adherence prediction in breast cancer survivors: A social cognitive theory based approach
Journal:
arXiv
Published Date:
Apr 20, 2025
Abstract
Non-adherence to medications is a critical concern since nearly half of
patients with chronic illnesses do not follow their prescribed medication
regimens, leading to increased mortality, costs, and preventable human
distress. Amongst stage 0-3 breast cancer survivors, adherence to long-term
adjuvant endocrine therapy (i.e., Tamoxifen and aromatase inhibitors) is
associated with a significant increase in recurrence-free survival. This work
aims to develop multi-scale models of medication adherence to understand the
significance of different factors influencing adherence across varying time
frames. We introduce a computational framework guided by Social Cognitive
Theory for multi-scale (daily and weekly) modeling of longitudinal medication
adherence. Our models employ both dynamic medication-taking patterns in the
recent past (dynamic factors) as well as less frequently changing factors
(static factors) for adherence prediction. Additionally, we assess the
significance of various factors in influencing adherence behavior across
different time scales. Our models outperform traditional machine learning
counterparts in both daily and weekly tasks in terms of both accuracy and
specificity. Daily models achieved an accuracy of 87.25%, and weekly models, an
accuracy of 76.04%. Notably, dynamic past medication-taking patterns prove most
valuable for predicting daily adherence, while a combination of dynamic and
static factors is significant for macro-level weekly adherence patterns.
