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

在重症监护中，迫切需要支持人工智能和机器学习（AI/ML）的基础设施。开发高分辨率的多中心数据集是迈向可操作和值得信赖的人工智能的关键第一步。作为NIH共同基金Bridge2AI计划的一部分，以患者为中心的协作医院存储库统一标准（CHoRUS）用于公平人工智能数据生成项目，将满足ML/AI应用生成数据的需求，这些应用旨在描述急性和危重症，预测并发症，并测量急性或危重症患者的治疗反应。通过6个模块，“以患者为中心的公平人工智能数据生成合唱”项目将解决与从10万多名危重患者中获取人工智能就绪数据集相关的多重挑战：1)团队科学，2)道德和可信赖的人工智能，3)标准，4)工具开发和优化，5)数据获取，6)技能和劳动力发展。该项目的总体目标是开发一个公开可用的、具有前所未有多样性的人工智能重症监护数据集，同时确保这些方法促进隐私、问责制、临床效益和公平，同时促进新一代人工智能临床医生和科学家的发展。该数据集还将包括一个抵抗测试集，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，以便在急性和重症监护中实施。

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.