2026-03-11 ワシントン大学セントルイス校
＜関連情報＞
- https://source.washu.edu/2026/03/surgical-ai-adapts-to-changing-patients/
- https://engineering.washu.edu/news/2026/Surgical-AI-adapts-to-changing-patients.html
- https://dl.acm.org/doi/10.1145/3788681
- https://dl.acm.org/doi/abs/10.1145/3534578
ウェアラブルデータと臨床データの適応的融合によるコホート変動への対応：膵臓手術結果予測に関する事例研究 Addressing Cohort Variability with Adaptive Fusion of Wearable and Clinical Data: A Case Study in Predicting Pancreatic Surgery Outcomes
Jingwen Zhang, Ruiqi Wang, Ziqi Xu, Hanyang Liu, Jorge Rodriguez, Heidy Cos, + 5Authors Info & Claims
ACM Transactions on Computing for Healthcare Published: 17 March 2026
DOI:https://doi.org/10.1145/3788681
Fig. 2.AdaMoE model architecture.
Abstract
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.
ウェアラブルデバイスを用いた術後合併症の予測：膵臓手術を受けた患者を対象とした症例研究 Predicting Post-Operative Complications with Wearables: A Case Study with Patients Undergoing Pancreatic Surgery
Jingwen Zhang, Dingwen Li, Ruixuan Dai, Heidy Cos, Gregory A. Williams, Lacey Raper, Chet W. Hammill, Chenyang Lu
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Published: 07 July 2022
DOI:https://doi.org/10.1145/3534578
Abstract
Post-operative complications and hospital readmission are of great concern to surgical patients and health care providers. Wearable devices such as Fitbit wristbands enable long-term and non-intrusive monitoring of patients outside clinical environments. To build accurate predictive models based on wearable data, however, requires effective feature engineering to extract high-level features from time series data collected by the wearable sensors. This paper presents a pipeline for developing clinical predictive models based on wearable sensors. The core of the pipeline is a multi-level feature engineering framework for extracting high-level features from fine-grained time series data. The framework integrates a set of techniques tailored for noisy and incomplete wearable data collected in real-world clinical studies: (1) singular spectrum analysis for extracting high-level features from daily features over the course of the study; (2) a set of daily features that are resilient to missing data in wearable time series data; (3) a K-Nearest Neighbors (KNN) method for imputing short missing heart rate segments; (4) the integration of patients’ clinical characteristics and wearable features. We evaluated the feature engineering approach and machine learning models in a clinical study involving 61 patients undergoing pancreatic surgery. Linear support vector machine (SVM) with integrated feature engineering achieved an AUROC of 0.8802 for predicting post-operative readmission or severe complications, which significantly outperformed the existing rule-based model used in clinical practice and other state-of-the-art feature engineering approaches.
