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.

迫切需要基础架构在重症监护中支持人工智能和机器学习（AI/ML）。开发高分辨率的多中心数据集是迈向可行和值得信赖的AI的关键第一步。作为NIH Common基金的Bridge2AI计划的一部分，以患者为中心的协作医院存储库标准（合唱）将满足AI数据生成项目的，将满足旨在为急性和重症监护疾病进行表征，预测复杂性并测量急性或重症患者的ML/AI应用程序生成数据的需求。通过6个模块，以公平AI数据生成项目为重点的患者合唱将解决与100,000多名重症患者的AI-Ready数据集有关的多重挑战：1）团队科学，2）伦理和可信赖的AI，3）标准，4）工具开发和优化，5）数据收购以及6）数据收购和工作和劳动力开发。该项目的总体目标是开发一个空前的多样性的公开可用的AI-Ready重症监护数据集，同时确保该方法促进隐私，问责制，临床福利和权益，同时促进新一代的AI临床医生和科学家。该数据集还将包括一个固定测试集，可用于模型外部验证，以帮助市场采用AI开发的模型以实现急性和重症监护。从潜水员，法律，道德，卫生服务，生物医学科学，工程和科学期刊出版物中的各种学科中汲取专业知识，该项目将a）建立一个法律框架，以大规模收集数据，采样以确保多样性并最小化偏见； b）执行面向社区的道德焦点小组以确定哪些数据适合公众共享； c）确保数据要素包括适当的健康社会决定者，以研究和了解护理提供的潜在偏见； d）在多中心开发功能，以获取，标准化，代币，存储，可视化和标记数据，包括结构化电子健康记录数据，标记化的非结构化电子健康记录数据，遥测和eeg波形，成像和社会决定者； e）获取数据，将数据标准化为OMOP公共数据模型，使用限制重新识别的差分隐私方法来转换数据，并标记数据以诊断和临床定义的事件； f）在外行和科学界培养专业知识，以通过多模式的教育方法来提高AI素养和利用。为此，该项目将涉及中心以及NIH Bridge2AI计划，NIH Bridge2ai桥中心，外部生物医学和临床组织，行业和监管机构之间的广泛合作。