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

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          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.

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            Clinical information extraction applications: A literature review

            With the rapid adoption of electronic health records (EHRs), it is desirable to harvest information and knowledge from EHRs to support automated systems at the point of care and to enable secondary use of EHRs for clinical and translational research. One critical component used to facilitate the secondary use of EHR data is the information extraction (IE) task, which automatically extracts and encodes clinical information from text.
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              Artificial Intelligence With Deep Learning Technology Looks Into Diabetic Retinopathy Screening.

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                Author and article information

                Contributors
                +507-293-0057 , Liu.Hongfang@mayo.edu
                +507-538-1191 , Fan.Jung-wei@mayo.edu
                Journal
                NPJ Digit Med
                NPJ Digit Med
                NPJ Digital Medicine
                Nature Publishing Group UK (London )
                2398-6352
                17 December 2019
                17 December 2019
                2019
                : 2
                : 130
                Affiliations
                [1 ]ISNI 0000 0004 0459 167X, GRID grid.66875.3a, Division of Digital Health Sciences, Department of Health Sciences Research, , Mayo Clinic, ; Rochester, MN USA
                [2 ]ISNI 0000 0004 0459 167X, GRID grid.66875.3a, Department of Cardiovascular Medicine, , Mayo Clinic, ; Rochester, MN USA
                [3 ]ISNI 0000 0004 0459 167X, GRID grid.66875.3a, Advanced Analytics Service Unit, Department of Information Technology, , Mayo Clinic, ; Rochester, MN USA
                Author information
                http://orcid.org/0000-0001-9090-8028
                http://orcid.org/0000-0003-1691-5179
                http://orcid.org/0000-0002-9191-3897
                http://orcid.org/0000-0002-9168-5855
                http://orcid.org/0000-0001-9202-0233
                http://orcid.org/0000-0001-9763-1164
                Article
                208
                10.1038/s41746-019-0208-8
                6917754
                31872069
                f0c4989f-f741-4656-8a96-ea0801e86679
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 5 September 2019
                : 25 November 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000002, U.S. Department of Health & Human Services | National Institutes of Health (NIH);
                Award ID: U01TR002062
                Award ID: U01TR002062
                Award ID: U01TR002062
                Award ID: U01TR002062
                Award ID: U01TR002062
                Award ID: U01TR002062
                Award ID: U01TR002062
                Award ID: U01TR002062
                Award Recipient :
                Funded by: U.S. Department of Health & Human Services | National Institutes of Health (NIH)
                Funded by: U.S. Department of Health & Human Services | National Institutes of Health (NIH)
                Funded by: U.S. Department of Health & Human Services | National Institutes of Health (NIH)
                Funded by: U.S. Department of Health & Human Services | National Institutes of Health (NIH)
                Funded by: U.S. Department of Health & Human Services | National Institutes of Health (NIH)
                Funded by: U.S. Department of Health & Human Services | National Institutes of Health (NIH)
                Funded by: U.S. Department of Health & Human Services | National Institutes of Health (NIH)
                Categories
                Perspective
                Custom metadata
                © The Author(s) 2019

                medical research,health care
                medical research, health care

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