Park Jung In, Johnson Steven, Pruinelli Lisiane
Sue & Bill Gross School of Nursing, University of California, Irvine, California, USA.
Institute for Health Informatics, University of Minnesota, Minneapolis, Minnesota, USA.
J Nurs Scholarsh. 2025 Jan;57(1):95-104. doi: 10.1111/jnu.13009. Epub 2024 Jul 26.
The aim of the study was to develop a prediction model using deep learning approach to identify breast cancer patients at high risk for chronic pain.
This study was a retrospective, observational study.
We used demographic, diagnosis, and social survey data from the NIH 'All of Us' program and used a deep learning approach, specifically a Transformer-based time-series classifier, to develop and evaluate our prediction model.
The final dataset included 1131 patients. We evaluated the deep learning prediction model, which achieved an accuracy of 72.8% and an area under the receiver operating characteristic curve of 82.0%, demonstrating high performance.
Our research represents a significant advancement in predicting chronic pain among breast cancer patients, leveraging deep learning model. Our unique approach integrates both time-series and static data for a more comprehensive understanding of patient outcomes.
Our study could enhance early identification and personalized management of chronic pain in breast cancer patients using a deep learning-based prediction model, reducing pain burden and improving outcomes.
本研究的目的是使用深度学习方法开发一种预测模型，以识别有慢性疼痛高风险的乳腺癌患者。
本研究是一项回顾性观察性研究。
我们使用了美国国立卫生研究院“我们所有人”项目中的人口统计学、诊断和社会调查数据，并使用深度学习方法，特别是基于Transformer的时间序列分类器，来开发和评估我们的预测模型。
最终数据集包括1131名患者。我们评估了深度学习预测模型，其准确率达到72.8%，受试者工作特征曲线下面积为82.0%，显示出高性能。
我们的研究代表了利用深度学习模型预测乳腺癌患者慢性疼痛方面的重大进展。我们独特的方法整合了时间序列和静态数据，以便更全面地了解患者的预后。
我们的研究可以使用基于深度学习的预测模型加强对乳腺癌患者慢性疼痛的早期识别和个性化管理，减轻疼痛负担并改善预后。
