# SCH: Screening and confirmatory machine learning for explainable modeling of non-cancer deaths in cancer patients

> **NIH NIH R37** · RUTGERS BIOMEDICAL AND HEALTH SCIENCES · 2022 · $288,332

## Abstract

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.

## Key facts

- **NIH application ID:** 10596376
- **Project number:** 1R37CA277812-01
- **Recipient organization:** RUTGERS BIOMEDICAL AND HEALTH SCIENCES
- **Principal Investigator:** Lanjing Zhang
- **Activity code:** R37 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $288,332
- **Award type:** 1
- **Project period:** 2022-09-01 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10596376

## Citation

> US National Institutes of Health, RePORTER application 10596376, SCH: Screening and confirmatory machine learning for explainable modeling of non-cancer deaths in cancer patients (1R37CA277812-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10596376. Licensed CC0.

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