Using machine learning to predict and reduce emergency care needs during cancer treatment
Official title: Multi-institutional validation of a multi-modal machine learning algorithm to predict and reduce acute care during cancer therapy
Funded at University of California, San Francisco · NIH-11026428 · Data updated 2026-11-12
This study is testing a smart computer program that helps doctors figure out when cancer patients might need extra care, like a trip to the emergency room, so they can get help sooner and feel better during their treatment.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of California, San Francisco NIH-funded |
| Lab location | 1 site (San Francisco, United States) |
| Project start | March 1, 2023 |
| Project end | February 29, 2028 |
| Funding year | 2025 |
| Award (this year) | $337,638 |
| NIH project number | 5R01CA277782-03 |
| Project ID | NIH-11026428 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are cancer patients receiving systemic therapy or radiation therapy who may be at risk for acute care needs.
✕Less likely to help
Patients not undergoing outpatient cancer therapy or those with stable conditions that do not require acute care may not benefit from this research.
What this research studies
This research aims to develop and validate a machine learning algorithm that can predict when cancer patients undergoing outpatient therapy might need acute care, such as emergency department visits or hospital admissions. By analyzing diverse patient data from various healthcare settings, the study seeks to improve the accuracy and fairness of these predictions. The goal is to enhance patient care by providing timely interventions that can reduce the need for acute care, ultimately improving treatment outcomes and reducing costs. Patients will be monitored for symptoms related to their cancer treatment, and the algorithm will help guide healthcare providers in making informed decisions.
Why it matters
Potential benefit: If successful, this research could lead to better management of cancer treatment side effects, reducing emergency care visits and improving overall patient outcomes.
How similar studies have gone: Previous studies have shown promising results using machine learning to predict healthcare needs in cancer patients, indicating that this approach has potential for success.
Where this research is happening
-
University of California, San Francisco
San Francisco, United States Active
Researchers
- Principal investigator: Hong, Julian Clint — University of California, San Francisco
- Study coordinator: Hong, Julian Clint
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About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.