Using machine learning to understand non-cancer deaths in cancer patients
SCH: Screening and confirmatory machine learning for explainable modeling of non-cancer deaths in cancer patients
This study is working on new computer tools to help predict non-cancer deaths in cancer patients, especially those with breast, colorectal, prostate, and lung cancers, so that we can better understand and support the growing number of cancer survivors who may face these risks.
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-10903982 on NIH RePORTER |
What this research studies
This research aims to develop advanced machine learning tools to accurately model and predict non-cancer deaths among cancer patients, particularly those with breast, colorectal, prostate, and lung cancers. By utilizing omic data and electronic health records, the project seeks to enhance the sensitivity and specificity of these models, addressing a critical gap in current healthcare practices. The approach involves creating separate screening and confirmatory machine learning tools to ensure reliable outcomes, ultimately improving patient care and understanding of mortality risks. This research is particularly focused on the growing population of cancer survivors who face increased risks of non-cancer-related deaths.
Who could benefit from this research
Good fit: Ideal candidates for this research include cancer patients, particularly those diagnosed with breast, colorectal, prostate, or lung cancers.
Not a fit: Patients with non-cancer-related conditions or those who are not currently undergoing cancer treatment may not benefit from this research.
Why it matters
Potential benefit: If successful, this research could lead to improved prediction and understanding of non-cancer deaths in cancer patients, potentially guiding better healthcare interventions.
How similar studies have performed: While machine learning has been applied in various healthcare contexts, this specific approach to modeling non-cancer deaths in cancer patients is relatively novel and untested.
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.