Addressing Cohort Variability with Adaptive Fusion of Wearable and Clinical Data: A Case Study in Predicting Pancreatic Surgery Outcomes

Résumé fourni par la source

Wearable devices, which continuously capture activity and physiological data, offer dynamic insights into patient health that complement traditional static clinical predictors. While integrating wearable and clinical data has shown promise for predicting clinical outcomes, the impact of cohort variability on model robustness remains underexplored. In this study, we investigate the impact of cohort variability on predictive model performance in a clinical study focused on predicting pancreatic surgery outcomes using data from Fitbit wristbands and clinical characteristics. This study, initiated before the COVID-19 pandemic and disrupted by surgery delays, highlights substantial variations in patient data before and after the pandemic. Our findings also show that the predictive utility of wearable and clinical features varies across patients. To address these challenges, we propose Adaptive Mixture of Experts (AdaMoE) , a Mixture of Experts model with a diversity regularization that adaptively adjusts the weighting of wearable and clinical features per patient. In a clinical study of 83 pancreatic surgery patients, our approach achieves improved performance compared to existing models and shows promise for handling cohort variability. This work underscores the importance of accounting for cohort variability in predictive modeling and suggests a pathway to enhance model robustness under cohort variability.

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