用日常运动记录和文字描述，用深度学习早筛代谢综合征。
Integrating Natural Language Processing and Exercise Monitoring for Early Diagnosis of Metabolic Syndrome: A Deep Learning Approach
- 融合文本分析与运动数据的深度学习模型。
- 模型在交叉验证中达到0.806的AUROC和76.3%召回率。
- 适合关注慢病早期预警的临床与健康管理人群。
代谢综合征（MetS）是一种以腹部肥胖、胰岛素抵抗、高血压和高脂血症为特征的慢性疾病，显著增加2型糖尿病等慢性病风险，影响全球约四分之一人口。目前标准诊断需依赖医疗机构血液检测，但常被忽视，导致未满足的医疗需求。本研究旨在利用日常生活中易获取的最少生理数据及运动相关自由文本，实现MetS的早期诊断。研究从40名养老院志愿者中收集数据，并采用数据增强缓解类别不平衡问题。提出一种结合自然语言处理（NLP）与运动监测的深度学习分类框架。结果表明，最佳模型在三折交叉验证中取得0.806的AUROC和76.3%的召回率。特征重要性分析显示，文本信息及每日最低心率对分类贡献最大。该研究展示了日常可测数据在早期诊断中的潜力，有望降低筛查与管理成本。
原文摘要 · Abstract (English)
Metabolic syndrome (MetS) is a medication condition characterized by abdominal obesity, insulin resistance, hypertension and hyperlipidemia. It increases the risk of majority of chronic diseases, including type 2 diabetes mellitus, and affects about one quarter of the global population. Therefore, early detection and timely intervention for MetS are crucial. Standard diagnosis for MetS components requires blood tests conducted within medical institutions. However, it is frequently underestimated, leading to unmet need for care for MetS population. This study aims to use the least physiological data and free texts about exercises related activities, which are obtained easily in daily life, to diagnosis MetS. We collected the data from 40 volunteers in a nursing home and used data augmentation to reduce the imbalance. We propose a deep learning framework for classifying MetS that integrates natural language processing (NLP) and exercise monitoring. The results showed that the best model reported a high positive result (AUROC=0.806 and REC=76.3%) through 3-fold cross-validation. Feature importance analysis revealed that text and minimum heart rate on a daily basis contribute the most in the classification of MetS. This study demonstrates the potential application of data that are easily measurable in daily life for the early diagnosis of MetS, which could contribute to reducing the cost of screening and management for MetS population.
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