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.

补充项目摘要 项目R 01 LM 014027 -01是做什么的？阿尔蒂官方情报有望改变 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 以更低的成本获得更好的治疗。与此同时，现代数字和移动的技术使其成为 更容易从大规模患者中收集信息。虽然“大”医疗数据提供了前所未有的 建立深度学习阿尔蒂神经网络（ANN）模型的机会，以推进 帕金森氏病（PD）等复杂疾病，也对患者数据隐私提出了独特的挑战。 本项目将开发基于随机正交变换的新型数据掩蔽技术 实现AI计算外包和数据共享，目标有两个：1）执行两个 在HiperGator云中训练具有数据掩蔽的ANN模型用于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， 实验研究中使用的ANN模型的训练性能。 我们为什么提出这个补充要求？东北大学丁爱东博士来访 2022年夏天的我们我们一起制作了一份手稿，扩展了我们的数据屏蔽方法与噪音 此外，当我们将医疗数据外包到云端用于AI模型时， 训练这是一个重大的进步，超越了最初提出的技术方法，但它 仍在研究计划的范围内。因此，我们要求一个补充项目，利用我们的新的 DiP方法转换两个PD数据集以保证差分隐私，并使其为AI做好准备， 基于云的机器学习研究。我们的分析表明，新的DiP方法可以改进 具有更少的噪声添加，这将导致更好的模型精度。我们打算带葛医生来 来自陶森大学的韩加入了球队。他在随机森林和扰动方面的专长可以帮助我们 减少噪音。项目R 01 LM 014027 -01两个目标的拟定补充任务如下。 目标1的补充任务：生成具有不同隐私的两个可共享PD数据集，并执行 机器学习案例研究：用于AI就绪性评估的DIP保护数据。我们将处理我们的 两个PD数据集与新的DiP方法，以确保不同的隐私。我们将进行一项实验 研究两个数据集，以评估从DiP保护数据中学习的PD诊断模型， 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的 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- 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 从DIP保护的数据中训练的ML/AI模型。