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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极低容忍度医疗实践，这对任何模型都要求极高的灵敏度和特异度。然而，几乎所有的机器学习(ML)模型都着眼于提高精度。它还不能同时到达两个地方使用医疗保健数据具有极高的灵敏度和特异度。单独筛查和确诊ML 为了达到非常高的灵敏度和特异度，提出了一些工具。此外，许多最大似然算法都会受到影响由于缺乏明确的解释，如深度学习和神经网络，不太可能满足公平的标准。癌症是美国第二大死因。癌症幸存者的数量继续增长；不幸的是，癌症患者的非癌症死亡人数也在增加。然而，几乎所有的经济学和大人口研究都侧重于两种结果(癌症死亡或复发)。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“体量和人口数据”。为了解决这些问题，该项目将开发筛选和验证性ML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使用电子数据和电子健康记录(EHR)的患者。拟议的研究将导致对ML工具、工作流程和方法的基本贡献，以创新地使用OMIC和EHR数据用于癌症护理。它及时满足了精准减少非癌症死亡的迫切需求。这个项目还独特地解决了变革性数据科学研究主题。跨学科协作计划中概述的在此项目中的协作将为创新提供多样化的基础问题的解决和验证。该提议有三个更广泛的影响：1)开发的小说ML 算法和技术将使医生能够更准确地预测和治疗癌症患者基于他们的多类别死亡风险。2)研究计划将支持和培育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进行研究的同时也意识到ML的有用性。相关性(请参阅说明)：建议的研究与公共卫生相关，因为开发和更好的利用是新颖的机器学习用于癌症患者非癌症死亡的分类有望降低发病率以及这些患者的死亡率。因此，拟议的研究与美国国立卫生研究院的任务部分相关这与发展基础知识有关，这将有助于延长人类寿命并减少人类疾病的负担。