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

• 批准号: 10596376
• 负责人: Lanjing Zhang
• 金额: $ 28.83万
• 依托单位: RUTGERS BIOMEDICAL AND HEALTH SCIENCES
• 依托单位国家: 美国
• 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/10596376
• 关键词: Address; Awareness; Cancer Model; Cancer Patient; Cancer Survivor; Cause of Death; Cessation of life; Collaborations; Colorectal Cancer; 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; Time; United States National Institutes of Health; Validation; base; cancer care; deep neural network; high school; improved; interdisciplinary collaboration; machine learning algorithm; machine learning model; malignant breast neoplasm; mortality; novel; programs; screening; tool; undergraduate student

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，主要的障碍是极低的错误容忍度 医疗保健实践，这需要任何建模的灵敏度和特异性。然而， 几乎所有机器学习（ML）模型都集中在提高准确性上。它还不能达到两者 使用医疗保健数据极高的灵敏度和特异性。单独筛选和确认性ML 提出了工具以达到非常高的灵敏度和特异性。而且，许多ML算法受到了 由于缺乏明确的解释，例如深度学习和神经网络，并且不太可能见面 公平标准。癌症是美国第二大死亡原因 继续增长；不幸的是，癌症患者的非癌症死亡人数也是如此。然而， 几乎所有的OMIC和大型人口研究都集中在二元结果（癌症死亡或复发）上。 因此，迫切需要更好地理解和减少癌症患者的非癌症死亡， 使用“ OMIC和人口数据。为了解决这些问题，该项目将开发筛查和 确认为乳腺癌，结直肠癌和肺癌中的癌症和非癌死亡建模的ML 使用“ OMIC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，工作流和方法的基本贡献，以新颖地使用“ OMIC和EHR数据” 用于癌症护理。它及时满足了精确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 在协作计划中概述的该项目中的协作将为创意提供多样化的基础 解决问题和验证。该提案具有3个更广泛的影响：1）发达的新型ML 算法和技术将使医生能够更精确地预测和治疗癌症 患者基于其多生死亡的风险。 2）研究计划将支持和培育 本科和研究生研究人员。 3）拟议的研究计划将支持高中和 本科生在进行研究和了解ML有用性方面。 相关性（请参阅说明）： 拟议的研究与公共卫生有关，因为开发和更好的利用率小说 预计癌症患者中非癌症死亡的机器学习有望降低发病率 和这些患者的死亡率。因此，拟议的研究与NIH使命的一部分有关 这与发展基本知识有关，这将有助于延长人类的生命并减少 人类疾病的负担。