基于低分辨率远程监测数据的AI心力衰竭恶化检测
AI-based detection of worsening heart failure from low-resolution telemonitoring data
另 1 家 · 查看机构详情
- University of Gothenburg（哥德堡大学）
- Sahlgrenska Academy（萨尔格伦斯卡学院）
- Sahlgrenska University Hospital（萨尔格伦斯卡大学医院）
- Institute of Medicine（医学研究所）
机构由 AI 辅助整理，请以论文原文为准。
浏览论文内容
中文总结 AI 辅助
本文提出TRACER模型，利用对比事件表示和Transformer架构，从低分辨率远程监测数据中预测心衰恶化导致的住院事件，在真实数据上准确率达66.7%，优于其他模型。
中文摘要 AI 辅助
目的：心力衰竭（HF）因其高合并症负担、老龄化患者群体和频繁住院而构成医疗挑战。远程监测通过早期检测健康恶化，为管理心衰患者提供了一种有前景的方法。开发自主系统以检测远程监测数据中的恶化迹象，对于减轻医疗人员的工作负担具有重要意义。方法：我们提出了TRACER模型，一种具有对比事件表示的Transformer，旨在预测低分辨率且不规则采样的远程监测数据中导致罕见住院事件的时间线。TRACER为每个生物标志物整合了时间感知嵌入，通过表示学习进行对比预训练以增强异常检测，并使用独立的二元分类器进行检测。我们使用了包含276名心衰患者远程记录生物标志物序列的测量数据，根据时间规则将其分割为重叠窗口，并根据窗口后缘是否发生心衰相关住院对窗口进行标记。结果：在高度不平衡的真实世界数据集中，TRACER能够正确预测66.7%导致心衰住院的时间线，高估率为7.9%。将TRACER的训练重新表述为事件检测问题，相比直接在预测窗口上训练，提高了预测性能，从而更有效地利用了有限的住院事件。结论：与其他测试模型相比，TRACER在检测真实世界远程监测数据中的恶化状态迹象方面表现出优越性能。意义：TRACER在识别临床恶化迹象方面显示出潜力，可生成警报以对心衰患者提供对抗性治疗。
英文摘要
Objective: Heart failure (HF) presents a healthcare challenge due to its high comorbidity burden, aging patient population and frequent hospitalizations. Remote monitoring offers a promising approach to managing HF patients by early detection of health deterioration. Developing autonomous systems to detect signs of worsening in telemonitoring data is of interest to reduce the workload of healthcare personnel. Methods: We propose the TRACER model, a Transformer with Contrastive Event Representation, designed to predict timelines leading to rare hospitalization events in low-resolution and irregularly sampled telemonitoring data. TRACER incorporates time-aware embeddings for each biomarker, contrastive pre-training to enhance anomaly detection via representation learning, and independent binary classifiers for detection. We used measurement data containing remotely recorded biomarker sequences from 276 HF patients segmented into overlapping windows based on temporal rules, and labeled the windows based on the occurrence of HF relevant hospitalizations at the latter edge of the window. Results: TRACER was able to correctly predict 66.7% timelines leading up to HF hospitalizations in the highly imbalanced real-world dataset with an overestimation of 7.9%. Reformulating the training of TRACER as an event detection problem improved the predictive performance compared with training directly on forecasting windows, enabling more effective use of the limited hospitalization events. Conclusion: TRACER demonstrated superior performance in detecting signs of worsening status in real-world telemonitoring data compared to the other tested models. Significance: TRACER shows promise in identifying signs of clinical deterioration that allow for alerts to be generated to provide counteractive treatment in patients with HF.
