|5R37CA277812-02 Interpret this number
|Rutgers Biomedical And Health Sciences
|SCH: Screening and Confirmatory Machine Learning for Explainable Modeling of Non-Cancer Deaths in Cancer Patients
Due to the high stakes of healthcare, the primary barrier is the extremely low tolerance of errors in
healthcare practice, which requires extremely high sensitivity and specificity of any modelling. However,
nearly all Machine learning (ML) models focus on improving the accuracy. It cannot yet reach both
extremely high sensitivity and specificity using healthcare data. Separate screening and confirmatory ML
tools are proposed to achieve very high sensitivity and specificity. Moreover, many ML algorithms suffer
from the lack of clear explanations, such as deep learning and neural networks, and would unlikely meet
the FAIR criteria. Cancer is the second leading cause of death in the U.S. The number of cancer survivors
continues to grow; unfortunately, so does the number of non-cancer deaths in cancer patients. However,
nearly all `omic and large population studies focused on binary outcomes (cancer death or recurrence).
Therefore, there is an urgent need to better understand and reduce non-cancer deaths in cancer patients,
using `omic and population data. To address these problems, the project will develop screening and
confirmatory ML to model cancer and noncancer deaths in breast, colorectal, prostate and lung cancer
patients using `omic data and electronic health records (EHR). The proposed research will result in
fundamental contribution to ML tools, workflows and methods to make novel use of `omic and EHR data
for cancer care. It timely meets the urgent needs in precise reduction of non-cancer deaths. This project
also uniquely addresses the Transformative Data Science research theme. The interdisciplinary
collaboration in this project as outlined in the Collaboration Plan will offer a diverse basis for creative
problem solving and validation. The proposal has 3 broader impacts: 1) The developed novel ML
algorithms and technology will enable physicians to more precisely prognosticate and treat cancer
patients based on their risk of multicategory deaths. 2) The research program will support and nurture
undergraduate and graduate researchers. 3) The proposed research program will support high school and
undergraduate students both in the conduct of research and in awareness of ML usefulness.
RELEVANCE (See instructions):
The proposed research is relevant to public health because the development and better utilization novel
machine learning for classifying non-cancer deaths in cancer patients is expected to reduce the morbidity
and mortality in these patients. Thus, the proposed research is relevant to the part of the NIH's mission
that pertains to developing fundamental knowledge that will help to lengthen human lives and reduce the
burdens of human illness.
Infection and inflammation stimulate expansion of a CD74+ Paneth cell subset to regulate disease progression.
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The EMBO journal, 2023-11-02; 42(21), p. e113975.