Bridge2AI: Patient-Focused Collaborative Hospital Repository Uniting Standards (CHoRUS) for Equitable AI

Bridge2AI：以患者为中心的协作医院存储库统一标准 (CHORUS)，实现公平的人工智能

基本信息

批准号：
10858694
负责人：
Azra Bihorac
金额：
$ 637.03万
依托单位：
MASSACHUSETTS GENERAL HOSPITAL
依托单位国家：
美国
项目类别：
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/10858694
关键词：
Accountability Acute Address Adoption Artificial Intelligence Biomedical Research Bridge to Artificial Intelligence Caring Clinical Collaborations Communities Critical Care Critical Illness Data Data Element Data Set Data Storage and Retrieval Deterioration Diagnosis Discipline Education Electroencephalography Electronic Health Record Engineering Ensure Equity Ethics Event Focus Groups Funding Generations Goals Health Services Hospitals Image Industry Infrastructure Journals Label Laws Legal Machine Learning Measures Methods Modeling Patient-Focused Outcomes Patients Privacy Publications Research Resolution Sampling Science Scientist Standardization Telemetry Testing United States National Institutes of Health Validation Visualization Workforce Development acute care care delivery data acquisition data modeling data standards data tools electronic structure improved literacy multimodality programs repository skill acquisition social health determinants tool tool development treatment response trustworthiness

项目摘要

There is an urgent need for infrastructure to support artificial intelligence and machine learning (AI/ML) in critical care. Developing high-resolution multi-center data sets is a critical first step towards actionable and trustworthy AI. As part of the NIH Common Fund’s Bridge2AI program, the Patient-Focused Collaborative Hospital Repository Uniting Standards (CHoRUS) for Equitable AI data generation project will meet the need of generating data for ML/AI applications aimed at characterizing acute and critical care illness, predicting complications, and measuring treatment response among patients with acute or critical illness. Through 6 modules, the Patient-Focused CHoRUS for Equitable AI data generation project will addresses multiple challenges relevant for acquiring an AI-ready data set from more than 100,000 critically ill patients: 1) Team Science, 2) Ethical and Trustworthy AI, 3) Standards, 4) Tool Development and Optimization, 5) Data Acquisition, and 6) Skill and Workforce Development. The project’s overarching goal is to develop a publicly available, AI-ready critical care dataset of unprecedented diversity, while ensuring the methods promote privacy, accountability, clinical benefit, and equity, while promoting a new generation of AI clinicians and scientists. The dataset will also include a holdout test set, accessible for model external validation to aid marketplace adoption of AI-developed models for implementation in acute and critical care. Drawing expertise from a diverse range of disciplines including team science, law, ethics, health services, biomedical science, engineering, and scientific journal publications, this project will A) establish a legal framework for collecting data at scale, sampling to ensure diversity and minimize bias; B) perform community-facing ethics focus groups to determine what data is appropriate for public sharing; C) ensure that data elements include appropriate social determinants of health to study and understand potential bias in care delivery; D) develop capabilities across a multi-center to acquire, standardize, tokenize, store, visualize, and label data including structured electronic health record data, tokenized unstructured electronic health record data, telemetry and EEG waveforms, imaging, and social determinants of health; E) acquire data, standardize data to the OMOP Common Data Model, transform data using differential privacy approaches that limit re-identification, and label data for diagnoses and events of clinical deterioration; and F) cultivate expertise in the lay and scientific community to improve AI literacy and utilization through multimodal educational approaches. To accomplish this, the project will involve extensive collaboration between centers as well as through the NIH Bridge2AI program, the NIH Bridge2AI Bridge Center, external biomedical and clinical organizations, industry, and regulatory agencies.

作为 NIH 共同基金的一部分，开发高分辨率多中心数据集是实现可操作且值得信赖的人工智能的关键第一步。 Bridge2AI 项目是公平人工智能数据生成项目的以患者为中心的协作医院存储库联合标准 (CHORUS)，将满足 ML/AI 应用程序生成数据的需求，旨在描述急性和危重症护理疾病的特征，通过 6 个模块，预测并发症并测量急性或危重疾病患者的治疗反应，以患者为中心的 CHoRUS for Equitable AI 数据生成项目将解决与从超过 100,000 名重症患者获取 AI 就绪数据集相关的多项挑战。：1) 团队科学，2) 道德和值得信赖的人工智能，3) 标准，4) 工具开发和优化，5) 数据采集，以及 6) 技能和劳动力该项目的总体目标是开发一个公开可用的、具有前所未有的多样性的人工智能重症监护数据集，确保这些方法促进隐私、问责制、临床利益和公平，同时促进新一代人工智能用户和科学家的发展。还将包括一个保留测试集，可用于模型外部验证，以帮助市场采用人工智能开发的模型来实施急症和重症护理。该项目汲取了团队科学、法律、伦理、卫生服务、生物医学科学、工程和科学期刊出版物等不同学科的专业知识，将 A) 建立一个大规模收集数据、抽样的法律框架，以确保多样性并最大限度地减少偏见； B) 开展面向社区的道德焦点小组，以确定哪些数据适合公开共享 C) 确保数据元素包含适当的健康社会决定因素，以研究和了解医疗服务中的潜在偏见； - 获取、标准化、标记化、存储的中心，可视化和标记数据，包括结构化电子健康记录数据、标记化非结构化电子健康记录数据、遥测和脑电图波形、成像和健康的社会决定因素；E) 获取数据，将数据标准化为 OMOP 通用数据模型，使用差分转换数据限制重新识别的隐私方法，并为诊断和临床恶化事件标记数据；F) 培养非专业人士和科学界的专业知识，通过多模式教育方法提高人工智能素养和利用。为了实现这一目标，该项目将涉及广泛的内容。合作中心之间以及通过 NIH Bridge2AI 计划、NIH Bridge2AI 桥接中心、外部生物医学和临床组织、行业和监管机构。