Predicting achievement of clinical goals using machine learning in myasthenia gravis

The developed MM diagnostic model can effectively predict MM or better status in MG patients, potentially guiding clinicians in determining treatment objectives and evaluating treatment outcomes.

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Publication Details

Journal
PLoS ONE
Published
2025-08-14
DOI
https://doi.org/10.1371/journal.pone.0330044
Primary Topic
Myasthenia Gravis and Thymoma
Type
article
Field-Weighted Citation Impact
0.00
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Predicting achievement of clinical goals using machine learning in myasthenia gravis

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Myasthenia Gravis and Thymoma
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Predicting achievement of clinical goals using machine learning in myasthenia gravis

Akiyuki Uzawa, Kimiaki Utsugisawa, Satoshi Kuwabara, Naoki Kawaguchi, Masayuki Masuda, Hiroyuki Akamine, Genya Watanabe, Tomoya Kubota, Yosuke Onishi, Manato Yasuda, Naoya Minami, Shingo Konno, Takashi Kimura, Yuriko Nagane, Yukiko Ozawa, Takamichi Sugimoto, Makoto Samukawa, Masanori Takahashi, Yasushi Suzuki, Shigeaki Suzuki
article en

Abstract

The developed MM diagnostic model can effectively predict MM or better status in MG patients, potentially guiding clinicians in determining treatment objectives and evaluating treatment outcomes.

PLoS ONEVol. 20(8)
Hiroshima University (JP), Toho University (JP), Tokyo Medical University (JP), Chiba University (JP), Keio University (JP), Toho University Ohashi Medical Center (JP), National Hospital Organization Hokkaido Medical Center (JP), Sendai Medical Center (JP), National Hospital Organization Hanamaki Hospital (JP), Chiba Hospital (JP), National Hospital Organization (JP), Hyogo University (JP), Kindai University (JP), The University of Osaka (JP)
Openalex Percentile: Top 70%
Myasthenia Gravis and Thymoma
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