Desiderata for delivering NLP to accelerate healthcare AI advancement and a Mayo Clinic NLP-as-a-service implementation
Open Access
- 17 December 2019
- journal article
- research article
- Published by Springer Science and Business Media LLC in npj Digital Medicine
- Vol. 2 (1), 1-7
- https://doi.org/10.1038/s41746-019-0208-8
Abstract
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.Keywords
Funding Information
- U.S. Department of Health & Human Services | National Institutes of Health (U01TR002062, U01TR002062, U01TR002062, U01TR002062, U01TR002062, U01TR002062, U01TR002062, U01TR002062)
- U.S. Department of Health & Human Services | National Institutes of Health
- U.S. Department of Health & Human Services | National Institutes of Health
- U.S. Department of Health & Human Services | National Institutes of Health
- U.S. Department of Health & Human Services | National Institutes of Health
- U.S. Department of Health & Human Services | National Institutes of Health
- U.S. Department of Health & Human Services | National Institutes of Health
- U.S. Department of Health & Human Services | National Institutes of Health
This publication has 33 references indexed in Scilit:
- Evaluation of an Automated Information Extraction Tool for Imaging Data Elements to Populate a Breast Cancer Screening RegistryJournal of Digital Imaging, 2015
- Emerging Spectra of Silent Brain InfarctionStroke, 2014
- A Hadoop-Based Method to Predict Potential Effective Drug CombinationBioMed Research International, 2014
- The epidemiology of silent brain infarction: a systematic review of population-based cohortsBMC Medicine, 2014
- HiveProceedings of the VLDB Endowment, 2009
- ConText: An algorithm for determining negation, experiencer, and temporal status from clinical reportsJournal of Biomedical Informatics, 2009
- BioTagger-GM: A Gene/Protein Name Recognition SystemJournal of the American Medical Informatics Association, 2009
- Silent brain infarcts: a systematic reviewThe Lancet Neurology, 2007
- Inverted files for text search enginesACM Computing Surveys, 2006
- UIMA: an architectural approach to unstructured information processing in the corporate research environmentNatural Language Engineering, 1999