A Methodology for a Scalable, Collaborative, and Resource-Efficient Platform, MERLIN, to Facilitate Healthcare AI Research.
A Methodology for a Scalable, Collaborative, and Resource-Efficient Platform, MERLIN, to Facilitate Healthcare AI Research.
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DOI：
10.1109/jbhi.2023.3259395
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发表时间：
2023-06
影响因子：
7.7
通讯作者：
中科院分区：
文献类型：
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医疗保健人工智能（AI）具有提高患者安全性、提高效率和改善患者预后的潜力，但研究往往受到数据访问、队列管理和分析工具的限制。电子健康记录数据、实时数据和实时高分辨率设备数据的收集和翻译可能具有挑战性且耗时。临床相关人工智能工具的开发需要克服数据采集、医院资源稀缺和数据治理要求方面的挑战。这些瓶颈可能导致人工智能系统的研究和开发的资源需求和长期延迟。我们提出了一种系统和方法来加速数据采集，数据集开发和分析以及AI模型开发。我们创建了一个依赖于可扩展微服务架构的交互式平台。该系统每小时可接收15，000条患者记录，每条记录代表数千个多模式测量、文本注释和高分辨率数据。这些记录加在一起可以达到TB级的数据。该平台可以在2-5分钟内进一步执行队列生成和初步数据集分析。因此，多个用户可以同时协作，以真实的时间对数据集和模型进行建模。我们预计，这种方法将加速临床AI模型的开发，从长远来看，将有意义地改善医疗服务。
Healthcare artificial intelligence (AI) holds the potential to increase patient safety, augment efficiency and improve patient outcomes, yet research is often limited by data access, cohort curation, and tools for analysis. Collection and translation of electronic health record data, live data, and real-time high-resolution device data can be challenging and time-consuming. The development of clinically relevant AI tools requires overcoming challenges in data acquisition, scarce hospital resources, and requirements for data governance. These bottlenecks may result in resource-heavy needs and long delays in research and development of AI systems. We present a system and methodology to accelerate data acquisition, dataset development and analysis, and AI model development. We created an interactive platform that relies on a scalable microservice architecture. This system can ingest 15,000 patient records per hour, where each record represents thousands of multimodal measurements, text notes, and high-resolution data. Collectively, these records can approach a terabyte of data. The platform can further perform cohort generation and preliminary dataset analysis in 2–5 minutes. As a result, multiple users can collaborate simultaneously to iterate on datasets and models in real time. We anticipate that this approach will accelerate clinical AI model development, and, in the long run, meaningfully improve healthcare delivery.