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.)

翻译：对于接受系统性癌症治疗的患者而言，门诊就诊之间的时段充满了不确定性以及副作用未被监测的风险。为弥补这一护理缺口，我们开发并前瞻性试验了一种用于远程患者监测（RPM）的多模态人工智能框架。该系统整合了来自HALO-X平台的多模态数据，如人口统计学信息、可穿戴传感器数据、每日调查问卷和临床事件。我们的观察性试验是同类研究中规模最大的之一，已从84名患者收集了超过210万个数据点（6,080个患者日）的监测数据。我们开发并调整了一个多模态人工智能模型，以处理真实世界RPM数据异步和不完整的特性，用于预测未来发生不良事件的持续风险。该模型的准确率达到83.9%（AUROC=0.70）。值得注意的是，模型识别出既往治疗史、健康检查记录和每日最大心率为关键预测特征。一项案例研究展示了该模型通过事件发生前输出逐步升级的风险概况来提供早期预警的能力。这项工作确立了多模态人工智能RPM在癌症护理中的可行性，并为实现更主动的患者支持提供了一条路径。（已被欧洲NeurIPS 2025医疗健康多模态表征学习研讨会接收。最佳论文海报奖。）