Two-step AI to predict non-cancer deaths in people with cancer
SCH: Screening and confirmatory machine learning for explainable modeling of non-cancer deaths in cancer patients
This project uses a two-step machine learning approach on health records and genetic data to help identify which cancer patients may be at risk of dying from non-cancer causes.
Quick facts
| Grant type | R37 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Rutgers Biomedical and Health Sciences NIH-funded |
| Lab location | 1 site (Newark, UNITED STATES) |
| Project ID | NIH-11210793 on NIH RePORTER |
What this research studies
From a patient's perspective, researchers will combine electronic health records and genomic ('omic') data from people with breast, colorectal, prostate, and lung cancer to look for patterns linked to non-cancer deaths. They plan to use a screening AI to flag many possible risks with very high sensitivity, then a confirmatory AI to narrow those flags with very high specificity. The team emphasizes explainable models so findings are understandable to doctors and meet FAIR data principles. The work aims to create tools and workflows that could be used in clinical care to better protect survivors from preventable non-cancer causes of death.
Who could benefit from this research
Good fit: Adults with breast, colorectal, prostate, or lung cancer who can share their medical records and genomic data with the research team would be the primary candidates for participation or data contribution.
Not a fit: People without those cancer types, those unwilling to share health or genetic data, or patients whose care does not generate accessible EHR or genomic records are unlikely to benefit directly from this project.
Why it matters
Potential benefit: If successful, this work could help clinicians identify patients at higher risk of non-cancer death so they can offer earlier prevention or tailored follow-up.
How similar studies have performed: While many AI models have been used to predict cancer outcomes, using separate screening and confirmatory explainable AI specifically to predict non-cancer deaths in cancer patients is relatively new.
Where this research is happening
Newark, UNITED STATES
- Rutgers Biomedical and Health Sciences — Newark, United States (Active)
Researchers
- Principal investigator: Zhang, Lanjing — Rutgers Biomedical and Health Sciences
- Study coordinator: Zhang, Lanjing
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 below.