Academic Journal
Short-term outcome prediction for myasthenia gravis: an explainable machine learning model
| العنوان: | Short-term outcome prediction for myasthenia gravis: an explainable machine learning model |
|---|---|
| المؤلفون: | Huahua Zhong, Zhe Ruan, Chong Yan, Zhiguo Lv, Xueying Zheng, Li-Ying Goh, Jianying Xi, Jie Song, Lijun Luo, Lan Chu, Song Tan, Chao Zhang, Bitao Bu, Yuwei Da, Ruisheng Duan, Huan Yang, Sushan Luo, Ting Chang, Chongbo Zhao |
| المصدر: | Therapeutic Advances in Neurological Disorders, Vol 16 (2023) |
| بيانات النشر: | SAGE Publishing, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Neurology. Diseases of the nervous system |
| مصطلحات موضوعية: | Neurology. Diseases of the nervous system, RC346-429 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1756-2864 17562864 |
| Relation: | https://doaj.org/toc/1756-2864 |
| DOI: | 10.1177/17562864231154976 |
| URL الوصول: | https://doaj.org/article/92d4c16e43ef4257a36b9b8b7366c27e |
| رقم الانضمام: | edsdoj.92d4c16e43ef4257a36b9b8b7366c27e |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 17562864 |
|---|---|
| DOI: | 10.1177/17562864231154976 |