Standardizing Extracted Data Using Automated Application of Controlled Vocabularies.
BACKGROUND: Extraction of toxicological end points from primary sources is a central component of systematic reviews and human health risk assessments. To ensure optimal use of these data, consistent language should be used for end point descriptions. However, primary source language describing trea...
| Publicado en: | Environmental Health Perspectives Vol. 132; no. 2; pp. 027006-1 - 27019 |
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| Autores principales: | , , , , , , , , , , , , , , , |
| Formato: | algorithm glossary research tables/charts Journal Article |
| Publicado: |
National Institute of Environmental Health Sciences
Feb2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=175957076&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 175957076 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00916765 3B5 jtl: Environmental Health Perspectives issn: 00916765 maglogo: N pubinfo: dt: Feb2024 vid: 132 iid: 2 pid: 56539 pub: National Institute of Environmental Health Sciences place: Research Triangle Park, North Carolina artinfo: ui: 175957076 175957076 175957076 10.1289/EHP13215 175957076 ppf: 027006-1 ppct: 13 formats: tig: atl: Standardizing Extracted Data Using Automated Application of Controlled Vocabularies. aug: au: Foster, Caroline Wignall, Jessica Kovach, Samuel Choksi, Neepa Allen, Dave Trgovcich, Joanne Rochester, Johanna R. Ceger, Patricia Daniel, Amber Hamm, Jon Truax, Jim Blake, Bevin McIntyre, Barry Sutherland, Vicki Stout, Matthew D. Kleinstreuer, Nicole affil: ICF, Durham, North Carolina, USA sug: subj: Automation Artificial Intelligence Utilization Vocabulary Standards Vocabulary, Controlled Human Male Female Unified Medical Language System Dose-Response Relationship Drug Toxicity Fetal Development Toxicity Tests Data Curation Data Analysis Software Reproductive Health Metadata Quality Assessment Descriptive Statistics Male Female ab: BACKGROUND: Extraction of toxicological end points from primary sources is a central component of systematic reviews and human health risk assessments. To ensure optimal use of these data, consistent language should be used for end point descriptions. However, primary source language describing treatment-related end points can vary greatly, resulting in large labor efforts to manually standardize extractions before data are fit for use. OBJECTIVES: To minimize these labor efforts, we applied an augmented intelligence approach and developed automated tools to support standardization of extracted information via application of preexisting controlled vocabularies. METHODS: We created and applied a harmonized controlled vocabulary crosswalk, consisting of Unified Medical Language System (UMLS) codes, German Federal Institute for Risk Assessment (BfR) DevTox harmonized terms, and The Organization for Economic Co-operation and Development (OECD) end point vocabularies, to roughly 34,000 extractions from prenatal developmental toxicology studies conducted by the National Toxicology Program (NTP) and 6,400 extractions from European Chemicals Agency (ECHA) prenatal developmental toxicology studies, all recorded based on the original study report language. RESULTS: We automatically applied standardized controlled vocabulary terms to 75% of the NTP extracted end points and 57% of the ECHA extracted end points. Of all the standardized extracted end points, about half (51%) required manual review for potential extraneous matches or inaccuracies. Extracted end points that were not mapped to standardized terms tended to be too general or required human logic to find a good match. We estimate that this augmented intelligence approach saved >350 hours of manual effort and yielded valuable resources including a controlled vocabulary crosswalk, organized related terms lists, code for implementing an automated mapping workflow, and a computationally accessible dataset. DISCUSSION: Augmenting manual efforts with automation tools increased the efficiency of producing a findable, accessible, interoperable, and reusable (FAIR) dataset of regulatory guideline studies. This open-source approach can be readily applied to other legacy developmental toxicology datasets, and the code design is customizable for other study types. pubtype: Academic Journal doctype: algorithm glossary research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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