Desiderata for delivering NLP to accelerate healthcare AI advancement and a Mayo Clinic NLP-as-a-service implementation

作者：Andrew Wen, Sunyang Fu, Sungrim Moon, Mohamed El Wazir, Andrew N. Rosenbaum, Vinod C. Kaggal, Sijia Liu, Sunghwan Sohn, Hongfang Liu, Jungwei Wilfred Fan · 发表于：npj Digital Medicine · 年份：2019 · DOI：10.1038/s41746-019-0208-8 · 被引用次数：147 · 研究领域：Artificial Intelligence in Healthcare and Education、Artificial Intelligence in Healthcare、Machine Learning in Healthcare

Data is foundational to high-quality artificial intelligence (AI). Given that a substantial amount of clinically relevant information is embedded in unstructured data, natural language processing (NLP) plays an essential role in extracting valuable information that can benefit decision making, administration reporting, and research. Here, we share several desiderata pertaining to development and usage of NLP systems, derived from two decades of experience implementing clinical NLP at the Mayo Clinic, to inform the healthcare AI community. Using a framework, we developed as an example implementation, the desiderata emphasize the importance of a user-friendly platform, efficient collection of domain expert inputs, seamless integration with clinical data, and a highly scalable computing infrastructure.