Using deep learning to predict how different treatments affect individual patients
Official title: Deep Learning for Individualized Treatment Effect Inference with Multimodal Depiction
Funded at Yale University · NIH-11094158 · Data updated 2026-11-12
This study is looking at how different treatments can help people with glioblastoma live longer by using smart computer technology to analyze patient information, so doctors can give personalized treatment advice that fits each person's needs.
Quick facts
| Grant type | R21 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Yale University NIH-funded |
| Lab location | 1 site (New Haven, United States) |
| Project start | June 1, 2024 |
| Project end | March 31, 2027 |
| Funding year | 2025 |
| Award (this year) | $188,438 |
| NIH project number | 5R21EB034911-02 |
| Project ID | NIH-11094158 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are patients diagnosed with glioblastoma who are considering different treatment options.
✕Less likely to help
Patients with conditions other than glioblastoma or those who are not undergoing treatment may not benefit from this research.
What this research studies
This research focuses on predicting how different treatments can impact the survival time of patients, particularly those with glioblastoma. By utilizing advanced deep learning algorithms, the study aims to analyze a wide range of patient data, including imaging and clinical information, to provide personalized treatment recommendations. The goal is to move beyond traditional methods by accurately estimating individualized treatment effects, which can help doctors make better treatment decisions tailored to each patient's unique situation.
Why it matters
Potential benefit: If successful, this research could lead to more effective and personalized treatment plans for patients, potentially improving survival outcomes.
How similar studies have gone: Other research has shown promise in using deep learning for treatment prediction, but this approach aims to address specific limitations in current methodologies, making it a novel effort.
Where this research is happening
-
Yale University
New Haven, United States Active
Researchers
- Principal investigator: Liu, Xiaofeng — Yale University
- Study coordinator: Liu, Xiaofeng
Related research
Other NIH-funded projects on the same topics.
-
Integrative histo-genomic analysis to guide cancer risk and targeted therapies with interpretable AI
Researchers are developing an advanced AI-powered approach to map genetic and cellular patterns across breast and cervical cancers to help tailor treatments.
-
Predicting how antimalarial drug resistance spreads with AI
A powerful AI model will be built to forecast how resistance to antimalarial drugs might spread in Africa and beyond, helping clinicians and public health teams stay ahead of outbreaks.
-
Self-supervised deep learning for biological sequences
A deep learning project uses self-supervised methods to better understand biological sequences and related data, with the goal of advancing biology and medicine.
-
Mobile phone imaging and AI to screen oral cancers in low-resource settings
A low-cost mobile phone device with artificial intelligence is being developed to help frontline health workers screen for oral cancer early in low-resource areas.
-
Linking Genetic Variants to Real-Life Health Effects with Smart AI
A new AI-powered approach will predict how genetic differences affect health across multiple levels to help doctors understand diseases and tailor care.
-
Integrative AI tools to map protein sequences to structures and functions
A scientist team will develop advanced AI methods to better connect protein sequences, their 3D shapes, and how they work, using large biological data from sequencing and structures.
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.