{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T15:38:38Z","timestamp":1785339518832,"version":"3.55.0"},"reference-count":43,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"China\u2019s National Natural Science Foundation","doi-asserted-by":"crossref","award":["No. 81870988, 82071410 and 82001335"],"award-info":[{"award-number":["No. 81870988, 82071410 and 82001335"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Shanghai Municipal Science and Technology Major Project","award":["No.2018SHZDZX01"],"award-info":[{"award-number":["No.2018SHZDZX01"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Ther Adv Neurol Disord"],"published-print":{"date-parts":[[2023,1]]},"abstract":"<jats:sec><jats:title>Background:<\/jats:title><jats:p> Myasthenia gravis (MG) is an autoimmune disease characterized by muscle weakness and fatigability. The fluctuating nature of the disease course impedes the clinical management. <\/jats:p><\/jats:sec><jats:sec><jats:title>Objective:<\/jats:title><jats:p> The purpose of the study was to establish and validate a machine learning (ML)\u2013based model for predicting the short-term clinical outcome in MG patients with different antibody types. <\/jats:p><\/jats:sec><jats:sec><jats:title>Methods:<\/jats:title><jats:p> We studied 890 MG patients who had regular follow-ups at 11 tertiary centers in China from 1 January 2015 to 31 July 2021 (653 patients for derivation and 237 for validation). The short-term outcome was the modified post-intervention status (PIS) at a 6-month visit. A two-step variable screening was used to determine the factors for model construction and 14 ML algorithms were used for model optimisation. <\/jats:p><\/jats:sec><jats:sec><jats:title>Results:<\/jats:title><jats:p> The derivation cohort included 653 patients from Huashan hospital [age 44.24 (17.22) years, female 57.6%, generalized MG 73.5%], and the validation cohort included 237 patients from 10 independent centers [age 44.24 (17.22) years, female 55.0%, generalized MG 81.2%]. The ML model identified patients who were improved with an area under the receiver operating characteristic curve (AUC) of 0.91 [0.89\u20130.93], \u2018Unchanged\u2019 0.89 [0.87\u20130.91], and \u2018Worse\u2019 0.89 [0.85\u20130.92] in the derivation cohort, whereas identified patients who were improved with an AUC of 0.84 [0.79\u20130.89], \u2018Unchanged\u2019 0.74 [0.67\u20130.82], and \u2018Worse\u2019 0.79 [0.70\u20130.88] in the validation cohort. Both datasets presented a good calibration ability by fitting the expectation slopes. The model is finally explained by 25 simple predictors and transferred to a feasible web tool for an initial assessment. <\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion:<\/jats:title><jats:p> The explainable, ML-based predictive model can aid in forecasting the short-term outcome for MG with good accuracy in clinical practice. <\/jats:p><\/jats:sec>","DOI":"10.1177\/17562864231154976","type":"journal-article","created":{"date-parts":[[2023,2,24]],"date-time":"2023-02-24T08:56:50Z","timestamp":1677229010000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":18,"title":["Short-term outcome prediction for myasthenia gravis: an explainable machine learning model"],"prefix":"10.1177","volume":"16","author":[{"given":"Huahua","family":"Zhong","sequence":"first","affiliation":[{"name":"Huashan Rare Disease Center, Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China"},{"name":"National Center 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