Supplement: SCH: Enabling Data Outsourcing and Sharing for AI-powered Parkinson's Research

补充：SCH：为人工智能驱动的帕金森病研究提供数据外包和共享

• 批准号: 10594084
• 负责人: Shigang Chen
• 金额: $ 28.13万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-03 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10594084
• 关键词: Artificial Intelligence; Biomedical Computing; Biomedical Research; Case Study; Cloud Computing; Communities; Complex; Data; Data Set; Diagnosis; Disease; Ensure; Evaluation; Foundations; Machine Learning; Manuscripts; Masks; Medical; Methods; Modeling; Modernization; Neural Network Simulation; Noise; Outcome; Outsourcing; Parkinson Disease; Parkinsonian Disorders; Patient Data Privacy; Patients; Performance; Privacy; Process; Public Health Informatics; Randomized; Readiness; Research; Technology; Training; Universities; Visit; accurate diagnosis; artificial neural network; base; biomedical informatics; cloud based; cost; data privacy; data sharing; deep learning; digital; disease diagnosis; experimental study; improved; mobile computing; novel; privacy protection; random forest; theories

Supplement Project Summary What does Project R01 LM014027-01 do? Artificial intelligence holds the promise of transforming data-driven biomedical research and computational health informatics for more accurate diagnosis and better treatment at lower cost. In the meantime, modern digital and mobile technologies make it much easier to collect information from patients in large scale. While “big” medical data offers unprecedented opportunities of building deep-learning artificial neural network (ANN) models to advance the research of complex diseases such as Parkinson's disease (PD), it also presents unique challenges to patient data privacy. This project will develop novel data masking technologies based on randomized orthogonal transformation to enable AI-computation outsourcing and data sharing, with the following two aims: 1) Perform two experimental studies of training ANN models with data masking in the HiperGator cloud for PD prediction and Parkinsonism diagnosis; 2) establish the theoretical foundation on data privacy, inference accuracy, and training performance of the ANN models used in the experimental studies. Why do we make this supplement request? Dr. Aidong Adam Ding from Northeastern University visited us in Summer 2022. Together we produced a manuscript that expanded our data masking method with noise addition to achieve differential privacy (DiP) when we outsource medical data to the cloud for AI model training. This is a significant advance that goes beyond the originally proposed technical approaches; yet it remains in the scope of the research plan. Therefore, we request a supplement project that utilizes our new DiP method to transform two PD data sets for guaranteed differential privacy and make them AI-ready for cloud-based machine learning studies. Our analysis has showed that the new DiP method could be improved with much less noise addition, which would result in much better model accuracy. We plan to bring Dr. Ge Han from Towson University into the team. His expertise in random forests and perturbations could help us reduce the noise. The proposed supplement tasks for the two aims of Project R01 LM014027-01 are below. Supplement Task to Aim 1: Produce two sharable PD data sets with differential privacy and perform a machine learning case study on the DiP-protected data for AI-readiness evaluation. We will process our two PD data sets with the new DiP method to ensure differential privacy. We will perform an experimental study over the two data sets to evaluate PD-diagnosis models learned from the DiP-protected data and to quantify the tradeoff between model accuracy and privacy protection, which helps us determine the best configuration of the privacy-protected data that we will share with the community. Supplement Task to Aim 2: Improve the DiP Method and Enhance the Quality of AI-Ready, DiP- Protected Data. We will refine the DiP method for less noise addition, which helps improve the accuracy of ML/AI models trained from the DiP-protected data.

补充项目概要 Project R01 LM014027-01 人工智能有望带来什么变革？ 数据驱动的生物医学研究和计算健康信息学，以实现更准确的诊断和 同时，现代数字和移动技术使其变得更加经济。 更容易大规模地收集患者信息，而“大”医疗数据提供了前所未有的优势。 构建深度学习人工神经网络（ANN）模型以推进研究的机会 帕金森病 (PD) 等复杂疾病的同时，也对患者数据隐私提出了独特的挑战。 该项目将开发基于随机正交变换的新型数据屏蔽技术 实现人工智能计算外包和数据共享，有以下两个目标： 1）执行两个 在 HiperGator 云中使用数据屏蔽训练 ANN 模型进行局部放电预测的实验研究 和帕金森病诊断；2）建立数据隐私、推理准确性和的理论基础； 实验研究中使用的 ANN 模型的训练性能。 东北大学丁爱东博士来访为何提出这个补充要求？ 2022 年夏天，我们共同撰写了一份手稿，扩展了我们的噪声数据屏蔽方法 当我们将医疗数据外包到云端用于人工智能模型时，除了实现差异隐私（DiP） 这是一个重大进步，超出了最初提出的技术方法； 仍然在研究计划的范围内，因此，我们请求一个利用我们新的补充项目。 DiP 方法转换两个 PD 数据集以保证差异隐私并使它们为 AI 做好准备 基于云的机器学习研究表明，新的 DiP 方法可以得到改进。 噪声的增加要少得多，这将导致更好的模型精度。 来自陶森大学的 Han 加入了团队，他在随机森林和扰动方面的专业知识可以帮助我们。 项目 R01 LM014027-01 的两个目标的拟议补充任务如下。 目标 1 的补充任务：生成两个具有差分隐私的可共享 PD 数据集并执行 用于 AI 就绪性评估的 DiP 保护数据的机器学习案例研究。 我们将使用新的 DiP 方法来确保差分隐私的两个 PD 数据集进行实验。 对两个数据集进行研究，以评估从 DiP 保护的数据中学习的 PD 诊断模型，并 量化模型准确性和隐私保护之间的权衡，这有助于我们确定最佳方案 配置我们将与社区共享的隐私保护数据。 目标 2 的补充任务：改进 DiP 方法并提高 AI-Ready、DiP- 的质量 受保护的数据。我们将改进 DiP 方法以减少噪声添加，这有助于提高准确性。 根据 DiP 保护的数据训练的 ML/AI 模型。