Saved in:
| Main Authors: | , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.00949 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911361248788480 |
|---|---|
| author | Liu, Yansong Stafford, Ronnie Khetrapal, Pramit Kocadag, Huriye Carvalho, Graça de Winter, Patricia Imran, Maryam Snook, Amelia Hadjivasiliou, Adamos Anand, D. Vijay Lin, Weining Kelly, John Zhou, Yukun Drobnjak, Ivana |
| author_facet | Liu, Yansong Stafford, Ronnie Khetrapal, Pramit Kocadag, Huriye Carvalho, Graça de Winter, Patricia Imran, Maryam Snook, Amelia Hadjivasiliou, Adamos Anand, D. Vijay Lin, Weining Kelly, John Zhou, Yukun Drobnjak, Ivana |
| contents | 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.) |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_00949 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-Modal AI for Remote Patient Monitoring in Cancer Care Liu, Yansong Stafford, Ronnie Khetrapal, Pramit Kocadag, Huriye Carvalho, Graça de Winter, Patricia Imran, Maryam Snook, Amelia Hadjivasiliou, Adamos Anand, D. Vijay Lin, Weining Kelly, John Zhou, Yukun Drobnjak, Ivana Machine Learning Artificial Intelligence 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.) |
| title | Multi-Modal AI for Remote Patient Monitoring in Cancer Care |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2512.00949 |