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Open Access

Correction

Correction: Creating a diagnostic assessment model for autism spectrum disorder by differentiating lexicogrammatical choices through machine learning

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Notice of Republication

This article was republished on March 13, 2025, to correct minor typographical errors that were introduced during the typesetting process. The publisher apologizes for these errors. Please download this article again to view the correct version. The originally published, uncorrected article and the republished, corrected articles are provided here for reference.

Supporting Information

S1 File. Originally published, uncorrected article.

https://doi.org/10.1371/journal.pone.0320613.s001

(PDF)

S2 File. Republished, corrected article.

https://doi.org/10.1371/journal.pone.0320613.s002

(PDF)

Reference

  1. 1. Kato S, Hanawa K, Saito M, Nakamura K. Creating a diagnostic assessment model for autism spectrum disorder by differentiating lexicogrammatical choices through machine learning. PLoS One. 2024;19(9): e0311209. pmid:39331681

Citation: The PLOS One Staff (2025) Correction: Creating a diagnostic assessment model for autism spectrum disorder by differentiating lexicogrammatical choices through machine learning. PLoS ONE 20(4): e0320613. https://doi.org/10.1371/journal.pone.0320613

Published: April 2, 2025

Copyright: © 2025 The PLOS One Staff. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.