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在多模式医疗保健中可视化临床数据检索和修复人工智能研究:关于RIL工作流程的技术说明

Ali Ganjizadeh^1,2, Stephanie J Zawada^1,3, Steve G Langer^1,2

¹Mayo Clinic Artificial Intelligence Laboratory, 200 1st Street SW, Rochester, MN, 55902, USA.

Journal of imaging informatics in medicine

|February 17, 2024

概括

一个新的RIL-workflow应用程序简化了医疗保健中人工智能 (AI) 的数据集成. 这个工具有效地检索和存储来自不同来源的患者数据,改进AI模型训练数据集.

科学领域:

医疗信息学医疗信息学
医疗保健中的人工智能
数据科学数据科学数据科学

背景情况:

数据策划和整合是开发医疗保健人工智能模型的重大挑战.
现有的工具很难有效地整合来自各种来源的异质临床数据.
需要改进的方法来检索和存储精选的患者数据.

研究的目的:

描述一个可定制的,模块化数据检索应用程序 (RIL-工作流) 集成各种临床数据.
展示RIL工作流程的可行性,用于创建人工智能研究的多式联络数据库.
评估用户对RIL工作流的可用性和有效性的反.

主要方法:

使用Camunda工作流自动化平台开发了一个模块化数据检索应用程序 (RIL工作流).
从快速医疗互操作资源 (FHIR) 和医学数字成像和通信 (DICOM) 来源的综合临床笔记,图像和处方数据.
利用基于Web的图形用户界面 (GUI) 来实现工作流自动化,错误分离和数据检索.

主要成果:

成功验证了RIL工作流的能力,以汇总,策划和管理来自多个数据源的错误.
创建了一个适合临床AI研究的多式联络数据库.
收集了用户反,突出了RIL工作流的优缺点.

结论:

RIL-workflow为整合异质临床数据提供了一个有效的解决方案,解决了人工智能研究中的一个关键瓶.
模块化设计和GUI为多模式AI训练数据集提供了可定制的数据检索和策划.
该应用程序的源代码是公开可用的,促进进一步开发和采用临床AI研究.

关键词:

临床研究是临床研究.医疗保健人工智能人工智能模块化应用程序模块化应用程序多式联运数据多式联运数据过程自动化过程自动化工作流程编排工作流程编排.

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