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

SCH：筛选和验证机器学习，用于癌症患者非癌症死亡的可解释模型

基本信息

批准号：
10689198
负责人：
Lanjing Zhang
金额：
$ 28.08万
依托单位：
RUTGERS BIOMEDICAL AND HEALTH SCIENCES
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-01 至 2026-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10689198
关键词：
Address Awareness Cancer Model Cancer Patient Cancer Survivor Categories Cause of Death Cessation of life Collaborations Colorectal Cancer Creativeness Data Data Science Development Electronic Health Record Healthcare Human Instruction Knowledge Machine Learning Malignant Neoplasms Malignant neoplasm of lung Malignant neoplasm of prostate Methods Mission Modeling Morbidity - disease rate Outcome Patients Physicians Population Population Study Problem Solving Public Health Recurrence Research Research Personnel Risk Sensitivity and Specificity Technology United States National Institutes of Health Validation cancer care deep learning high school improved interdisciplinary collaboration learning network machine learning algorithm machine learning classification machine learning model malignant breast neoplasm mortality neural network novel prognostication programs screening tool undergraduate student

项目摘要

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.

由于医疗保健的高风险，主要障碍是对错误的容忍度极低，医疗保健实践，这需要非常高的灵敏度和特异性的任何建模。然而，在这方面，几乎所有的机器学习（ML）模型都专注于提高准确性。它还不能同时达到这两个目标使用医疗保健数据的极高灵敏度和特异性。单独筛选和确证性ML 提出了实现非常高的灵敏度和特异性的工具。此外，许多ML算法遭受由于缺乏明确的解释，如深度学习和神经网络，公平的标准。癌症是美国第二大死亡原因。继续增长;不幸的是，癌症患者中的非癌症死亡人数也在增长。然而，在这方面，几乎所有“组学”和大人群研究都集中在二元结果（癌症死亡或复发）上。因此，迫切需要更好地了解和减少癌症患者的非癌症死亡，使用`经济和人口数据。为了解决这些问题，该项目将开发筛选和用于模拟乳腺癌、结直肠癌、前列腺癌和肺癌的癌症和非癌症死亡的确证性ML 患者使用“组学数据”和电子健康记录。这项研究将导致对机器学习工具、工作流程和方法的基本贡献，以新颖地使用“omic”和EHR数据 for cancer癌症care护理.及时满足了精准降低非癌症死亡的迫切需求。这个项目它还独特地解决了变革性数据科学研究主题。跨学科合作计划中概述的本项目的合作将为创意提供多样化的基础解决问题和验证。该提案有3个更广泛的影响：1）开发的新ML 算法和技术将使医生能够更精确地诊断和治疗癌症根据患者的多类死亡风险。2)该研究项目将支持和培养本科生和研究生研究人员。3)拟议的研究计划将支持高中和本科生在研究的进行和ML有用性的认识。相关性（参见说明）：拟议的研究与公共卫生有关，因为开发和更好地利用新颖的用于分类癌症患者非癌症死亡的机器学习有望降低癌症患者的发病率。和死亡率之间的关系因此，拟议的研究与NIH的部分使命相关这涉及到发展基本知识，这将有助于延长人的寿命，减少人类疾病的负担。