用多模态AI分析癌症患者远程监测数据，提前预警不良事件。
Multi-Modal AI for Remote Patient Monitoring in Cancer Care
- 融合可穿戴设备、问卷和临床数据，构建异步不完整数据的多模态预测模型。
- 在84名患者210万条数据上实现83.9%准确率，AUROC达0.70。
- 识别出心率、既往治疗等关键预警特征，适合肿瘤远程护理研究者参考。
接受系统性癌症治疗的患者在两次门诊之间存在不确定性与未监测副作用的风险。为弥合这一护理空白，我们开发并前瞻性试验了一套用于远程患者监测（RPM）的多模态AI框架。该系统整合来自HALO-X平台的多模态数据，包括人口统计学信息、可穿戴传感器、每日问卷和临床事件。本观察性试验是同类中规模最大的之一，共收集了超过210万条数据点（6,080患者日），覆盖84名患者。我们开发并适配了一种多模态AI模型，以处理真实世界RPM数据的异步性和不完整性，持续预测未来不良事件风险。模型准确率达83.9%（AUROC=0.70）。值得注意的是，模型识别出既往治疗、健康自查和每日最大心率是关键预测特征。案例研究显示，模型能通过递增的风险评分在事件发生前提供早期预警。本工作证实了多模态AI在癌症护理中远程监测的可行性，并为更主动的患者支持提供了路径。（已入选欧洲NeurIPS 2025多模态表征学习医疗研讨会，获最佳海报论文奖。）
原文摘要 · Abstract (English)
For patients undergoing systemic cancer therapy, the time between clinic visits is full of uncertainties and risks of unmonitored side effects. To bridge this gap in care, we developed and prospectively trialed a multi-modal AI framework for remote patient monitoring (RPM). This system integrates multi-modal data from the HALO-X platform, such as demographics, wearable sensors, daily surveys, and clinical events. Our observational trial is one of the largest of its kind and has collected over 2.1 million data points (6,080 patient-days) of monitoring from 84 patients. We developed and adapted a multi-modal AI model to handle the asynchronous and incomplete nature of real-world RPM data, forecasting a continuous risk of future adverse events. The model achieved an accuracy of 83.9% (AUROC=0.70). Notably, the model identified previous treatments, wellness check-ins, and daily maximum heart rate as key predictive features. A case study demonstrated the model's ability to provide early warnings by outputting escalating risk profiles prior to the event. This work establishes the feasibility of multi-modal AI RPM for cancer care and offers a path toward more proactive patient support.(Accepted at Europe NeurIPS 2025 Multimodal Representation Learning for Healthcare Workshop. Best Paper Poster Award.)
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