UNIPHY+用统一模型实现从医院到家庭的连续健康监测。
A Unified AI Approach for Continuous Monitoring of Human Health and Diseases from Intensive Care Unit to Home with Physiological Foundation Models (UNIPHY+)
- 通过多模态融合与知识蒸馏，统一处理不同场景生理数据。
- 在重症监护与居家监测中均展现可泛化、可扩展的性能。
- 适合临床决策支持与长期健康管理场景使用。
我们提出UNIPHY+，一种统一的生理学基础模型（physioFM）框架，旨在利用普遍可获取的生理数据，在从重症监护室到居家环境的多种医疗场景中实现对人类健康与疾病的连续监测。该框架提出新颖的预训练、微调及轻量化个性化策略，结合多模态学习、特征融合微调和知识蒸馏技术，有效融入上下文信息。我们倡导在广泛的应用场景中测试UNIPHY+，涵盖重症监护与移动监测，以证明其能够推动通用、可扩展且个性化的生理人工智能发展，支持临床决策与长期健康追踪。
原文摘要 · Abstract (English)
We present UNIPHY+, a unified physiological foundation model (physioFM) framework designed to enable continuous human health and diseases monitoring across care settings using ubiquitously obtainable physiological data. We propose novel strategies for incorporating contextual information during pretraining, fine-tuning, and lightweight model personalization via multi-modal learning, feature fusion-tuning, and knowledge distillation. We advocate testing UNIPHY+ with a broad set of use cases from intensive care to ambulatory monitoring in order to demonstrate that UNIPHY+ can empower generalizable, scalable, and personalized physiological AI to support both clinical decision-making and long-term health monitoring.
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