Short-term outcome prediction for myasthenia gravis: an explainable machine learning model
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 predi...
| Main Authors: | , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2023-02-01
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| Series: | Therapeutic Advances in Neurological Disorders |
| Online Access: | https://doi.org/10.1177/17562864231154976 |
| _version_ | 1828004677033132032 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Huahua Zhong |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-10T07:19:47Z |
| format | Article |
| id | doaj.art-92d4c16e43ef4257a36b9b8b7366c27e |
| institution | Directory Open Access Journal |
| issn | 1756-2864 |
| language | English |
| last_indexed | 2024-04-10T07:19:47Z |
| publishDate | 2023-02-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Therapeutic Advances in Neurological Disorders |
| spelling | doaj.art-92d4c16e43ef4257a36b9b8b7366c27e2023-02-24T15:03:28ZengSAGE PublishingTherapeutic Advances in Neurological Disorders1756-28642023-02-011610.1177/17562864231154976Short-term outcome prediction for myasthenia gravis: an explainable machine learning modelHuahua ZhongZhe RuanChong YanZhiguo LvXueying ZhengLi-Ying GohJianying XiJie SongLijun LuoLan ChuSong TanChao ZhangBitao BuYuwei DaRuisheng DuanHuan YangSushan LuoTing ChangChongbo Zhao 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.https://doi.org/10.1177/17562864231154976 |
| spellingShingle | 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 Short-term outcome prediction for myasthenia gravis: an explainable machine learning model Therapeutic Advances in Neurological Disorders |
| title | Short-term outcome prediction for myasthenia gravis: an explainable machine learning model |
| title_full | Short-term outcome prediction for myasthenia gravis: an explainable machine learning model |
| title_fullStr | Short-term outcome prediction for myasthenia gravis: an explainable machine learning model |
| title_full_unstemmed | Short-term outcome prediction for myasthenia gravis: an explainable machine learning model |
| title_short | Short-term outcome prediction for myasthenia gravis: an explainable machine learning model |
| title_sort | short term outcome prediction for myasthenia gravis an explainable machine learning model |
| url | https://doi.org/10.1177/17562864231154976 |
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