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      Using a Natural Language Processing Approach to Support Rapid Knowledge Acquisition

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          Abstract

          Implementing artificial intelligence to extract insights from large, real-world clinical data sets can supplement and enhance knowledge management efforts for health sciences research and clinical care. At Vanderbilt University Medical Center (VUMC), the in-house developed Word Cloud natural language processing system extracts coded concepts from patient records in VUMC’s electronic health record repository using the Unified Medical Language System terminology. Through this process, the Word Cloud extracts the most prominent concepts found in the clinical documentation of a specific patient or population. The Word Cloud provides added value for clinical care decision-making and research. This viewpoint paper describes a use case for how the VUMC Center for Knowledge Management leverages the condition-disease associations represented by the Word Cloud to aid in the knowledge generation needed to inform the interpretation of phenome-wide association studies.

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          Most cited references29

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          The Unified Medical Language System (UMLS): integrating biomedical terminology.

          Olivier Bodenreider (2004)
          The Unified Medical Language System (http://umlsks.nlm.nih.gov) is a repository of biomedical vocabularies developed by the US National Library of Medicine. The UMLS integrates over 2 million names for some 900,000 concepts from more than 60 families of biomedical vocabularies, as well as 12 million relations among these concepts. Vocabularies integrated in the UMLS Metathesaurus include the NCBI taxonomy, Gene Ontology, the Medical Subject Headings (MeSH), OMIM and the Digital Anatomist Symbolic Knowledge Base. UMLS concepts are not only inter-related, but may also be linked to external resources such as GenBank. In addition to data, the UMLS includes tools for customizing the Metathesaurus (MetamorphoSys), for generating lexical variants of concept names (lvg) and for extracting UMLS concepts from text (MetaMap). The UMLS knowledge sources are updated quarterly. All vocabularies are available at no fee for research purposes within an institution, but UMLS users are required to sign a license agreement. The UMLS knowledge sources are distributed on CD-ROM and by FTP.
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            PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene–disease associations

            Joshua C Denny, Marylyn D Ritchie, Melissa Basford … (2010)
            Motivation: Emergence of genetic data coupled to longitudinal electronic medical records (EMRs) offers the possibility of phenome-wide association scans (PheWAS) for disease–gene associations. We propose a novel method to scan phenomic data for genetic associations using International Classification of Disease (ICD9) billing codes, which are available in most EMR systems. We have developed a code translation table to automatically define 776 different disease populations and their controls using prevalent ICD9 codes derived from EMR data. As a proof of concept of this algorithm, we genotyped the first 6005 European–Americans accrued into BioVU, Vanderbilt's DNA biobank, at five single nucleotide polymorphisms (SNPs) with previously reported disease associations: atrial fibrillation, Crohn's disease, carotid artery stenosis, coronary artery disease, multiple sclerosis, systemic lupus erythematosus and rheumatoid arthritis. The PheWAS software generated cases and control populations across all ICD9 code groups for each of these five SNPs, and disease-SNP associations were analyzed. The primary outcome of this study was replication of seven previously known SNP–disease associations for these SNPs. Results: Four of seven known SNP–disease associations using the PheWAS algorithm were replicated with P-values between 2.8 × 10−6 and 0.011. The PheWAS algorithm also identified 19 previously unknown statistical associations between these SNPs and diseases at P < 0.01. This study indicates that PheWAS analysis is a feasible method to investigate SNP–disease associations. Further evaluation is needed to determine the validity of these associations and the appropriate statistical thresholds for clinical significance. Availability:The PheWAS software and code translation table are freely available at http://knowledgemap.mc.vanderbilt.edu/research. Contact: josh.denny@vanderbilt.edu
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              Phenome-Wide Association Studies

              Lisa Bastarache, Joshua C Denny, Dan Roden (2022)
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                Author and article information

                Contributors
                Taneya Y Koonce:
                ORCID: https://orcid.org/0000-0002-4014-467X
                Center for Knowledge ManagementVanderbilt University Medical Center3401 West EndSuite 304Nashville, TN, 37203United States1 6159365790taneya.koonce@vumc.org
                Journal
                Journal ID (nlm-ta): JMIR Med Inform
                Journal ID (iso-abbrev): JMIR Med Inform
                Journal ID (publisher-id): JMI
                Title: JMIR Medical Informatics
                Publisher: JMIR Publications (Toronto, Canada )
                ISSN (Electronic): 2291-9694
                Publication date Collection: 2024
                Publication date (Electronic): 30 January 2024
                Volume: 12
                Electronic Location Identifier: e53516
                Affiliations
                [1 ] Center for Knowledge Management Vanderbilt University Medical Center Nashville, TN United States
                [2 ] Department of Biomedical Informatics Vanderbilt University School of Medicine Vanderbilt University Medical Center Nashville, TN United States
                Author notes
                Corresponding Author: Taneya Y Koonce taneya.koonce@ 123456vumc.org
                Author information
                https://orcid.org/0000-0002-4014-467X
                https://orcid.org/0000-0002-2677-6734
                https://orcid.org/0000-0002-2526-3857
                https://orcid.org/0000-0003-0356-9481
                https://orcid.org/0000-0002-3081-6487
                https://orcid.org/0000-0001-6699-6806
                https://orcid.org/0000-0002-7644-9803
                Article
                Publisher ID: v12i1e53516
                DOI: 10.2196/53516
                PMC ID: 10865202
                PubMed ID: 38289670
                SO-VID: d0996c48-75ff-4701-905a-c1e915509272
                Copyright © ©Taneya Y Koonce, Dario A Giuse, Annette M Williams, Mallory N Blasingame, Poppy A Krump, Jing Su, Nunzia B Giuse. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 30.01.2024.
                License:

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on https://medinform.jmir.org/, as well as this copyright and license information must be included.

                History
                Date received : 10 October 2023
                Date revision requested : 8 December 2023
                Date revision received : 15 December 2023
                Date accepted : 4 January 2024
                Categories
                Subject: Viewpoint
                Subject: Viewpoint

                Keywords: natural language processing,electronic health records,machine learning,data mining,knowledge management,nlp
                Data availability:
                Keywords: natural language processing, electronic health records, machine learning, data mining, knowledge management, nlp

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