Administrative Supplement: Improving AI/ML-Readiness of data generated from HALBE or other NIH-funded research

项目摘要 目前，在我们的母项目HABLE的生物储存库中收集和共享的数据继续扩大 迅速人工智能和机器学习（AI/ML）无法使这些数据对生物医学有价值 研究直到这些数据是AI/ML就绪。因此，迫切需要开发有效的AI/ML 准备好HABLE和其他NIH资助的数据共享项目。 该提案将侧重于三个关键和共同领域，以改善所生成数据的AI/ML准备情况 来自我们的母HABLE项目：缺失数据插补，特征选择和离群值删除，以及数据 准备报告我们将解决以下三个具体目标：目标1）开发一个基于机器学习的 处理缺失数据的多重插补方法;目标2）开发递归特征消除和 用于特征选择和离群值去除的交叉验证（RFE-CV）算法，以及目标3）开发一个 报告数据准备情况的集成工具。 该应用程序中的算法和工具将是第一个报告NIH数据准备情况的同类工具- 共享项目以促进AI/ML的异构数据和特征工程。它将使数据科学家 更有效、更轻松地改进他们的AI/ML建模。行政补充项目将 不仅有利于母HABLE项目，也有利于所有其他NIH资助的数据共享项目。我们预计 随着算法和工具的开发，我们将在HABLE项目中完成高数据准备， 这将最终使HABLE在开发治疗阿尔茨海默氏症的最先进方法方面更具创新性 疾病（AD）的临床试验，导致有效的个性化治疗的发展， 预防和治疗AD。

PROJECT SUMMARY Currently, data collected and shared in the biorepository of our parent project HABLE continues to expand rapidly. Artificial Intelligence and Machine Learning (AI/ML) cannot make these data valuable to biomedical research until these data are AI/ML-ready. Therefore, there is an urgent need to develop effective AI/ML readiness for HABLE and other NIH-funded data-sharing projects. This proposal will focus on three critical and common areas to improve the AI/ML-readiness of data generated from our parent HABLE project: missing data imputation, feature selection and outlier removal, and data readiness report. We will address the following three specific aims: Aim 1) Develop a Machine Learning Based Multiple Imputation Method for Handling Missing Data; Aim 2) Develop a Recursive Feature Elimination and Cross-Validation (RFE-CV) Algorithm for Feature Selection and Outlier removal, and Aim 3) Develop an Integrated Tool to Report Data Readiness. The algorithms and tools from this application will be the first of their kind to report data readiness for NIH data- sharing projects to facilitate heterogeneous data and feature engineering for AI/ML. It will make data scientists improve their AI/ML modeling more effectively and effortlessly. The administrative supplement project will benefit not only the parent HABLE project but also all other NIH-funded data-sharing projects. We expect that with the development of the algorithms and tools we will complete high data readiness in the HABLE project, which will eventually make HABLE more innovative in developing state of the art methods for Alzheimer’s Disease (AD) clinical trials, leading to the development of effective personalized treatments which slow the progression of, and prevent, AD.