Grant Number:	3R37CA277812-03S1 Interpret this number
Primary Investigator:	Zhang, Lanjing
Organization:	Rutgers Biomedical And Health Sciences
Project Title:	Screening and Confirmatory Machine Learning for Explainable Modeling of Non-Cancer Deaths in Cancer Patients
Fiscal Year:	2024

Abstract This supplement proposal is closely related the parent grant for two major reasons: 1). Breast cancer patients with disabilities fared worse than those without disabilities. The same occurred in non-small cell cancer or cervical cancer patients with disabilities. However, it is unclear how to best use ML tools to address this health disparity issue. As a direct extension of the parent grant’s overarching goal that focuses on all cancer patients, the proposed work will exclusively focus on the cancer patients with disabilities and plan to improve ML performance and survival rates in these patients. 2). Based on reported incident cancer cases in patients with disabilities and all incident cancer cases about 27-31% of the colorectal, lung or prostate cancer patients in the SEER-Medicare dataset had disabilities at the time of cancer diagnosis. Therefore, the proposed research will shed light on how modelling a minority population differs from modelling the whole population in a real-world dataset using our novel ML algorithms. Thus, it is a direct extension of parent grant’s Aim 1.2 that focuses on impact of imbalanced label-distribution. There are four major barriers that we feel significantly limit the research and career potential of the candidate, who is an Early Stage Investigator (ESI) and has a documented disability. Without this supplement focusing on these barriers, she will unlikely successfully compete for a R01 grant or become an independent investigator. Upon completion of this supplement, we expect to overcome the four major barriers, build a solid research foundation and generate ample preliminary data to successfully compete for R01 grants.





None. See parent grant details.