Validating the knowledge represented by a self-organizing map with an expert-derived knowledge structure

Amos, Andrew James, Lee, Kyungmi, Sen Gupta, Tarun, and Malau-Aduli, Bunmi S. (2024) Validating the knowledge represented by a self-organizing map with an expert-derived knowledge structure. BMC Medical Education, 24. 416.

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Abstract

Background: Professionals are reluctant to make use of machine learning results for tasks like curriculum development if they do not understand how the results were generated and what they mean. Visualizations of peer reviewed medical literature can summarize enormous amounts of information but are difficult to interpret. This article reports the validation of the meaning of a self-organizing map derived from the Medline/PubMed index of peer reviewed medical literature by its capacity to coherently summarize the references of a core psychiatric textbook.

Methods: Reference lists from ten editions of Kaplan and Sadock's Comprehensive Textbook of Psychiatry were projected onto a self-organizing map trained on Medical Subject Headings annotating the complete set of peer reviewed medical research articles indexed in the Medline/PubMed database (MedSOM). K-means clustering was applied to references from every edition to examine the ability of the self-organizing map to coherently summarize the knowledge contained within the textbook.

Results: MedSOM coherently clustered references into six psychiatric knowledge domains across ten editions (1967–2017). Clustering occurred at the abstract level of broad psychiatric practice including General/adult psychiatry, Child psychiatry, and Administrative psychiatry.

Conclusions: The uptake of visualizations of published medical literature by medical experts for purposes like curriculum development depends upon validation of the meaning of the visualizations. The current research demonstrates that a self-organizing map (MedSOM) can validate the stability and coherence of the references used to support the knowledge claims of a standard psychiatric textbook, linking the products of machine learning to a widely accepted standard of knowledge.

Item ID: 85305
Item Type: Article (Research - C1)
ISSN: 1472-6920
Copyright Information: © The Author(s) 2024. 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
Date Deposited: 01 May 2025 00:23
FoR Codes: 39 EDUCATION > 3901 Curriculum and pedagogy > 390110 Medicine, nursing and health curriculum and pedagogy @ 100%
SEO Codes: 16 EDUCATION AND TRAINING > 1699 Other education and training > 169999 Other education and training not elsewhere classified @ 50%
20 HEALTH > 2099 Other health > 209999 Other health not elsewhere classified @ 50%
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