Background:Myasthenia gravis (MG) is an autoimmune disease characterized by muscle weakness and fatigability. The fluctuating nature of the disease course impedes the clinical management. Objective:The purpose of the study was to establish and validate a machine learning (ML)-based model for predicting the short-term clinical outcome in MG patients with different antibody types. Methods: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. Results: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 [0.89-0.93], 'Unchanged' 0.89 [0.87-0.91], and 'Worse' 0.89 [0.85-0.92] in the derivation cohort, whereas identified patients who were improved with an AUC of 0.84 [0.79-0.89], 'Unchanged' 0.74 [0.67-0.82], and 'Worse' 0.79 [0.70-0.88] 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. Conclusion:The explainable, ML-based predictive model can aid in forecasting the short-term outcome for MG with good accuracy in clinical practice.