2025-06-20 ワシントン大学セントルイス校
<関連情報>
- https://source.washu.edu/2025/06/predicting-pain-with-machine-learning/
- https://engineering.washu.edu/news/2025/Predicting-pain-with-machine-learning.html
- https://dl.acm.org/doi/10.1145/3729488
モバイルセンシングと臨床データを用いた予測モデルにおける不確実性の組み込み: 手術後の持続的な痛みに関するケーススタディ Incorporating Uncertainty in Predictive Models Using Mobile Sensing and Clinical Data: A Case Study on Persistent Post-surgical Pain
Ziqi Xu, Jingwen Zhang, Simon Haroutounian, Hanyang Liu, Zihan Cao, Gabrielle Rose Messner, + 7
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Published: 18 June 2025
DOI:https://doi.org/10.1145/3729488
Abstract
Persistent postoperative pain (PPSP) is a significant concern in perioperative care, affecting patient quality of life and clinical outcomes. While traditional clinical data may capture patient-reported and objective physiological data in clinical settings, Ecological Momentary Assessment (EMA) collected using smartphones offers dynamic insights into patients’ mental and emotional states in their natural environments. However, the self-reported nature of EMA introduces significant variability in both data quality and patient compliance. This variability is often overlooked in studies, where EMA data is treated as contributing equally for all patients. In this paper, we propose an end-to-end approach that addresses this challenge by first quantifying the prediction uncertainty for each data source, including both EMA and clinical data. Next, we integrate the uncertainties into the final decision-making process by assigning more weight to more reliable data sources at the individual level, improving overall predictive performance. Compared to traditional models, our model provides uncertainty estimates for each decision, offering clinicians critical insight into the trustworthiness of predictions. Finally, we conduct both qualitative and quantitative analysis to demonstrate the impact of our uncertainty estimates. Validated through a prospective clinical study involving 782 patients undergoing surgery, our approach achieves superior predictive performance and calibration in PPSP prediction. This work is expected to contribute to personalized perioperative care by effectively integrating traditional clinical and mobile sensing data through an end-to-end uncertainty-aware framework.
