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.

补充项目总结