{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T04:12:12Z","timestamp":1788235932061,"version":"build-2803163510"},"reference-count":52,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2025,8,14]],"date-time":"2025-08-14T00:00:00Z","timestamp":1755129600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: Myasthenia gravis (MG), a chronic autoimmune disorder with variable disease trajectories, presents considerable challenges for clinical stratification and acute care management. This systematic review evaluated machine learning models developed for prognostic assessment in patients with MG. Methods: Following PRISMA guidelines, we systematically searched PubMed, Embase, and Scopus for relevant articles published from January 2010 to May 2025. Studies using machine learning techniques to predict MG-related outcomes based on structured or semi-structured clinical variables were included. We extracted data on model targets, algorithmic strategies, input features, validation design, performance metrics, and interpretability methods. The risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool. Results: Eleven studies were included, targeting ICU admission (n = 2), myasthenic crisis (n = 1), treatment response (n = 2), prolonged mechanical ventilation (n = 1), hospitalization duration (n = 1), symptom subtype clustering (n = 1), and artificial intelligence (AI)-assisted examination scoring (n = 3). Commonly used algorithms included extreme gradient boosting, random forests, decision trees, multivariate adaptive regression splines, and logistic regression. Reported AUC values ranged from 0.765 to 0.944. Only two studies employed external validation using independent cohorts; others relied on internal cross-validation or repeated holdout. Of the seven prognostic modeling studies, four were rated as having high risk of bias, primarily due to participant selection, predictor handling, and analytical design issues. The remaining four studies focused on unsupervised symptom clustering or AI-assisted examination scoring without predictive modeling components. Conclusions: Despite promising performance metrics, constraints in generalizability, validation rigor, and measurement consistency limited their clinical application. Future research should prioritize prospective multicenter studies, dynamic data sharing strategies, standardized outcome definitions, and real-time clinical workflow integration to advance machine learning-based prognostic tools for MG and support improved patient care in acute settings.<\/jats:p>","DOI":"10.3390\/diagnostics15162044","type":"journal-article","created":{"date-parts":[[2025,8,14]],"date-time":"2025-08-14T15:16:37Z","timestamp":1755184597000},"page":"2044","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Machine Learning in Myasthenia Gravis: A Systematic Review of Prognostic Models and AI-Assisted Clinical Assessments"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6743-6667","authenticated-orcid":false,"given":"Chen-Chih","family":"Chung","sequence":"first","affiliation":[{"name":"Department of Neurology, Taipei Medical University Shuang Ho Hospital, New Taipei City 235, Taiwan"},{"name":"Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan"},{"name":"Research & Innovation Center of Artificial Intelligence in Medicine and Health, Shuang Ho Hospital, New Taipei City 235, Taiwan"},{"name":"Taipei Neuroscience Institute, Taipei Medical University Shuang Ho Hospital, New Taipei City 235, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1610-4995","authenticated-orcid":false,"given":"I-Chieh","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Neurology, Taipei Medical University Shuang Ho Hospital, New Taipei City 235, Taiwan"},{"name":"Taipei Neuroscience Institute, Taipei Medical University Shuang Ho Hospital, New Taipei City 235, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8229-0408","authenticated-orcid":false,"given":"Oluwaseun Adebayo","family":"Bamodu","sequence":"additional","affiliation":[{"name":"Department of Prevention and Community Health, Milken Institute School of Public Health, The George Washington University, Washington, DC 20052, USA"},{"name":"Directorate of Postgraduate Studies, School of Clinical Medicine, Muhimbili University of Health and Allied Sciences, Ilala District, Dar es Salaam P.O. Box 65001, Tanzania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chien-Tai","family":"Hong","sequence":"additional","affiliation":[{"name":"Department of Neurology, Taipei Medical University Shuang Ho Hospital, New Taipei City 235, Taiwan"},{"name":"Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan"},{"name":"Research & Innovation Center of Artificial Intelligence in Medicine and Health, Shuang Ho Hospital, New Taipei City 235, Taiwan"},{"name":"Taipei Neuroscience Institute, Taipei Medical University Shuang Ho Hospital, New Taipei City 235, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2712-8533","authenticated-orcid":false,"given":"Hou-Chang","family":"Chiu","sequence":"additional","affiliation":[{"name":"Department of Neurology, Taipei Medical University Shuang Ho Hospital, New Taipei City 235, Taiwan"},{"name":"Taipei Neuroscience Institute, Taipei Medical University Shuang Ho Hospital, New Taipei City 235, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2570","DOI":"10.1056\/NEJMra1602678","article-title":"Myasthenia Gravis","volume":"375","year":"2016","journal-title":"N. Engl. J. Med."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.1016\/S1474-4422(15)00145-3","article-title":"Myasthenia Gravis: Subgroup Classification and Therapeutic Strategies","volume":"14","author":"Gilhus","year":"2015","journal-title":"Lancet Neurol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1002\/mus.27832","article-title":"Myasthenic Crisis","volume":"68","author":"Claytor","year":"2023","journal-title":"Muscle Nerve"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e299","DOI":"10.1212\/WNL.0000000000008688","article-title":"Stetefeld. Myasthenic Crisis Demanding Mechanical Ventilation: A Multicenter Analysis of 250 Cases","volume":"94","author":"Neumann","year":"2020","journal-title":"Neurology"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wang, Y., Huan, X., Zhu, X., Song, J., Yan, C., Yang, L., Xi, C., Xu, Y., Xi, J., and Zhao, C. (2024). Independent Risk Factors for in-Hospital Outcome of Myasthenic Crisis: A Prospective Cohort Study. Ther. Adv. Neurol. Disord., 17.","DOI":"10.1177\/17562864241226745"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Schneider-Gold, C., Hagenacker, T., Melzer, N., and Ruck, T. (2019). Understanding the Burden of Refractory Myasthenia Gravis. Ther. Adv. Neurol. Disord., 12.","DOI":"10.1177\/1756286419832242"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1007\/s40120-024-00619-4","article-title":"Epidemiology, Patient Characteristics, and Treatment Patterns of Myasthenia Gravis in Taiwan: A Population-Based Study","volume":"13","author":"Tsai","year":"2024","journal-title":"Neurol. Ther."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1623","DOI":"10.1007\/s00415-019-09518-3","article-title":"Artificial Intelligence as an Emerging Technology in the Current Care of Neurological Disorders","volume":"268","author":"Patel","year":"2021","journal-title":"J. Neurol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1038\/s41582-020-0377-8","article-title":"Applications of Machine Learning to Diagnosis and Treatment of Neurodegenerative Diseases","volume":"16","author":"Myszczynska","year":"2020","journal-title":"Nat. Rev. Neurol."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Hong, C.T., Bamodu, O.A., Chiu, H.W., Chiu, W.T., Chan, L., and Chung, C.C. (2025). Personalized Predictions of Therapeutic Hypothermia Outcomes in Cardiac Arrest Patients with Shockable Rhythms Using Explainable Machine Learning. Diagnostics, 15.","DOI":"10.3390\/diagnostics15030267"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Chung, C.C., Su, E.C.Y., Chen, J.H., Chen, Y.T., and Kuo, C.Y. (2023). Xgboost-Based Simple Three-Item Model Accurately Predicts Outcomes of Acute Ischemic Stroke. Diagnostics, 13.","DOI":"10.3390\/diagnostics13050842"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1347","DOI":"10.1056\/NEJMra1814259","article-title":"Machine Learning in Medicine","volume":"380","author":"Rajkomar","year":"2019","journal-title":"N. Engl. J. Med."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Bamodu, O.A., Chan, L., Wu, C.H., Yu, S.F., and Chung, C.C. (2024). Beyond Diagnosis: Leveraging Routine Blood and Urine Biomarkers to Predict Severity and Functional Outcome in Acute Ischemic Stroke. Heliyon, 10.","DOI":"10.1016\/j.heliyon.2024.e26199"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Band, S.S., Yarahmadi, A., Hsu, C.-C., Biyari, M., Sookhak, M., Ameri, R., Dehzangi, I., Chronopoulos, A.T., and Liang, H.-W. (2023). Application of Explainable Artificial Intelligence in Medical Health: A Systematic Review of Interpretability Methods. Inform. Med. Unlocked, 40.","DOI":"10.1016\/j.imu.2023.101286"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Chou, S.-Y., Bamodu, O.A., Chiu, W.-T., Hong, C.-T., Chan, L., and Chung, C.-C. (2022). Artificial Neural Network-Boosted Cardiac Arrest Survival Post-Resuscitation in-Hospital (Caspri) Score Accurately Predicts Outcome in Cardiac Arrest Patients Treated with Targeted Temperature Management. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-11201-z"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","article-title":"From Local Explanations to Global Understanding with Explainable Ai for Trees","volume":"2","author":"Lundberg","year":"2020","journal-title":"Nat. Mach. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Chang, C.C., Yeh, J.H., Chiu, H.C., Chen, Y.M., Jhou, M.J., Liu, T.C., and Lu, C.J. (2022). Utilization of Decision Tree Algorithms for Supporting the Prediction of Intensive Care Unit Admission of Myasthenia Gravis: A Machine Learning-Based Approach. J. Pers. Med., 12.","DOI":"10.3390\/jpm12010032"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kuo, C.-Y., Su, E.C.-Y., Yeh, H.-L., Yeh, J.-H., Chiu, H.-C., and Chung, C.-C. (2024). Predictive Modeling and Interpretative Analysis of Risks of Instability in Patients with Myasthenia Gravis Requiring Intensive Care Unit Admission. Heliyon, 10.","DOI":"10.1016\/j.heliyon.2024.e41084"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhong, H., Ruan, Z., Yan, C., Lv, Z., Zheng, X., Goh, L.-Y., Xi, J., Song, J., Luo, L., and Chu, L. (2023). Short-Term Outcome Prediction for Myasthenia Gravis: An Explainable Machine Learning Model. Ther. Adv. Neurol. Disord., 16.","DOI":"10.1177\/17562864231154976"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Chang, C.C., Yeh, J.H., Chiu, H.C., Liu, T.C., Chen, Y.M., Jhou, M.J., and Lu, C.J. (2023). Assessing the Length of Hospital Stay for Patients with Myasthenia Gravis Based on the Data Mining Mars Approach. Front Neurol., 14.","DOI":"10.3389\/fneur.2023.1283214"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Xu, Y., Li, Q., Pan, M., Jia, X., Wang, W., Guo, Q., and Luan, L. (2024). Interpretable Machine Learning Models for Predicting Short-Term Prognosis in Achr-Ab+ Generalized Myasthenia Gravis Using Clinical Features and Systemic Inflammation Index. Front. Neurol., 15.","DOI":"10.3389\/fneur.2024.1459555"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Bershan, S., Meisel, A., and Mergenthaler, P. (2025). Data-Driven Explainable Machine Learning for Personalized Risk Classification of Myasthenic Crisis. Int. J. Med. Inf., 194.","DOI":"10.1016\/j.ijmedinf.2024.105679"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2875","DOI":"10.1007\/s00415-024-12208-4","article-title":"Polar: Prediction of Prolonged Mechanical Ventilation in Patients with Myasthenic Crisis","volume":"271","author":"Heider","year":"2024","journal-title":"J. Neurol."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Steyaert, S., Lootus, M., Sarabu, C., Framroze, Z., Dickinson, H., Lewis, E., Steels, J.C., and Rinaldo, F. (2023). A Decentralized, Prospective, Observational Study to Collect Real-World Data from Patients with Myasthenia Gravis Using Smartphones. Front. Neurol., 14.","DOI":"10.3389\/fneur.2023.1144183"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Lesport, Q., Palmie, D., \u00d6ztosun, G., Kaminski, H.J., and Garbey, M. (2024). Ai-Powered Telemedicine for Automatic Scoring of Neuromuscular Examinations. Bioengineering, 11.","DOI":"10.3390\/bioengineering11090942"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Garbey, M., Lesport, Q., Girma, H., \u00d6ztosun, G., Abu-Rub, M., Guidon, A.C., Juel, V., Nowak, R.J., Soliven, B., and Aban, I. (2024). Application of Digital Tools and Artificial Intelligence in the Myasthenia Gravis Core Examination. Front Neurol., 15.","DOI":"10.3389\/fneur.2024.1474884"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1002\/mus.28394","article-title":"A Quantitative Study of Factors Influencing Myasthenia Gravis Telehealth Examination Score","volume":"72","author":"Garbey","year":"2025","journal-title":"Muscle Nerve"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Page, M.J., McKenzie, J.E., Bossuyt, P.M., Boutron, I., Hoffmann, T.C., Mulrow, C.D., Shamseer, L., Tetzlaff, J.M., Akl, E.A., and Brennan, S.E. (2021). The Prisma 2020 Statement: An Updated Guideline for Reporting Systematic Reviews. BMJ, 372.","DOI":"10.1136\/bmj.n71"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"51","DOI":"10.7326\/M18-1376","article-title":"Probast: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies","volume":"170","author":"Wolff","year":"2019","journal-title":"Ann. Intern. Med."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Liu, Z., Zhu, Y., Yuan, Y., Yang, L., Wang, K., Wang, M., Yang, X., Wu, X., Tian, X., and Zhang, R. (2021). 3d Densenet Deep Learning Based Preoperative Computed Tomography for Detecting Myasthenia Gravis in Patients with Thymoma. Front. Oncol., 11.","DOI":"10.3389\/fonc.2021.631964"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1097\/MNM.0000000000001547","article-title":"Combined Clinical and Specific Positron Emission Tomography\/Computed Tomography-Based Radiomic Features and Machine-Learning Model in Prediction of Thymoma Risk Groups","volume":"43","author":"Ozkan","year":"2022","journal-title":"Nucl. Med. Commun."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Liu, W., Wang, W., Guo, R., Zhang, H., and Guo, M. (2024). Deep Learning for Risk Stratification of Thymoma Pathological Subtypes Based on Preoperative Ct Images. BMC Cancer, 24.","DOI":"10.1186\/s12885-024-12394-4"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Abgrall, G., Holder, A.L., Chelly Dagdia, Z., Zeitouni, K., and Monnet, X. (2024). Should Ai Models Be Explainable to Clinicians?. Crit. Care, 28.","DOI":"10.1186\/s13054-024-05005-y"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Gonzalez, F.A., Santonocito, C., Lamas, T., Costa, P., Vieira, S.M., Ferreira, H.A., and Sanfilippo, F. (2024). Is Artificial Intelligence Prepared for the 24-H Shifts in the Icu?. Anaesth. Crit. Care Pain Med., 43.","DOI":"10.1016\/j.accpm.2024.101431"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Shamsutdinova, D., Stamate, D., and Stahl, D. (2025). Balancing Accuracy and Interpretability: An R Package Assessing Complex Relationships Beyond the Cox Model and Applications to Clinical Prediction. Int. J. Med. Inform., 194.","DOI":"10.1016\/j.ijmedinf.2024.105700"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Cutillo, C.M., Sharma, K.R., Foschini, L., Kundu, S., Mackintosh, M., and Mandl, K.D. (2020). Machine Intelligence in Healthcare-Perspectives on Trustworthiness, Explainability, Usability, and Transparency. npj Digit. Med., 3.","DOI":"10.1038\/s41746-020-0254-2"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1548","DOI":"10.1212\/WNL.0b013e3181a41211","article-title":"Incidence and Mortality Rates of Myasthenia Gravis and Myasthenic Crisis in Us Hospitals","volume":"72","author":"Alshekhlee","year":"2009","journal-title":"Neurology"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1109","DOI":"10.1007\/s10072-014-1659-y","article-title":"Predictors of Outcome of Myasthenic Crisis","volume":"35","author":"Kalita","year":"2014","journal-title":"Neurol. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Liu, F., Wang, Q., and Chen, X. (2019). Myasthenic Crisis Treated in a Chinese Neurological Intensive Care Unit: Clinical Features, Mortality, Outcomes, and Predictors of Survival. BMC Neurol., 19.","DOI":"10.1186\/s12883-019-1384-5"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Nelke, C., Stascheit, F., Eckert, C., Pawlitzki, M., Schroeter, C.B., Huntemann, N., Mergenthaler, P., Arat, E., \u00d6zt\u00fcrk, M., and Foell, D. (2022). Independent Risk Factors for Myasthenic Crisis and Disease Exacerbation in a Retrospective Cohort of Myasthenia Gravis Patients. J. Neuroinflammation, 19.","DOI":"10.1186\/s12974-022-02448-4"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Rajkomar, A., Oren, E., Chen, K., Dai, A.M., Hajaj, N., Hardt, M., Liu, P.J., Liu, X., Marcus, J., and Sun, M. (2018). Scalable and Accurate Deep Learning with Electronic Health Records. npj Digit. Med., 1.","DOI":"10.1038\/s41746-018-0029-1"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Li, Y., Rao, S., Solares, J.R.A., Hassaine, A., Ramakrishnan, R., Canoy, D., Zhu, Y., Rahimi, K., and Salimi-Khorshidi, G. (2020). Behrt: Transformer for Electronic Health Records. Sci. Rep., 10.","DOI":"10.1038\/s41598-020-62922-y"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1016\/j.nmd.2020.06.010","article-title":"Fatigue in Patients with Myasthenia Gravis. A Systematic Review of the Literature","volume":"30","author":"Ruiter","year":"2020","journal-title":"Neuromuscul. Disord."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Marbin, D., Piper, S.K., Lehnerer, S., Harms, U., and Meisel, A. (2022). Mental Health in Myasthenia Gravis Patients and Its Impact on Caregiver Burden. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-22078-3"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Gelinas, D., Parvin-Nejad, S., Phillips, G., Cole, C., Hughes, T., Silvestri, N., Govindarajan, R., Jefferson, M., Campbell, J., and Burnett, H. (2022). The Humanistic Burden of Myasthenia Gravis: A Systematic Literature Review. J. Neurol. Sci., 437.","DOI":"10.1016\/j.jns.2022.120268"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Severcan, F., Ozyurt, I., Dogan, A., Severcan, M., Gurbanov, R., Kucukcankurt, F., Elibol, B., Tiftikcioglu, I., Gursoy, E., and Yangin, M.N. (2024). Decoding Myasthenia Gravis: Advanced Diagnosis with Infrared Spectroscopy and Machine learning. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-66501-3"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1314","DOI":"10.1002\/acn3.51823","article-title":"Assessing Facial Weakness in Myasthenia Gravis with Facial Recognition Software and Deep Learning","volume":"10","author":"Ruiter","year":"2023","journal-title":"Ann. Clin. Transl. Neurol."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Zhou, G., Wang, S., Lin, L., Lu, K., Lin, Z., Zhang, Z., Zhang, Y., Cheng, D., Szeto, K., and Peng, R. (2024). Screening for Immune-Related Biomarkers Associated with Myasthenia Gravis and Dilated Cardiomyopathy Based on Bioinformatics Analysis and Machine Learning. Heliyon, 10.","DOI":"10.1016\/j.heliyon.2024.e28446"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Liu, H., Liu, G., Guo, R., Li, S., and Chang, T. (2024). Identification of Potential Key Genes for the Comorbidity of Myasthenia Gravis with Thymoma by Integrated Bioinformatics Analysis and Machine Learning. Bioinform. Biol. Insights, 18.","DOI":"10.1177\/11779322241281652"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Sha, Q., Zhang, Z., Li, H., Xu, Y., Wang, J., and Du, A. (2024). Serum Metabolomic Profile of Myasthenia Gravis and Potential Values as Biomarkers in Disease Monitoring. Clin. Chim. Acta, 562.","DOI":"10.1016\/j.cca.2024.119873"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1572","DOI":"10.1016\/j.csbj.2024.04.025","article-title":"Explainable Machine Learning Model for Identifying Key Gut Microbes Metabolites Biomarkers Associated with Myasthenia Gravis","volume":"23","author":"Chang","year":"2024","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1007\/s40258-024-00897-x","article-title":"Predictors of High Healthcare Cost among Patients with Generalized Myasthenia Gravis: A Combined Machine Learning and Regression Approach from a Us Payer Perspective","volume":"22","author":"Zhdanava","year":"2024","journal-title":"Appl. Health Econ. Health Policy"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/16\/2044\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:27:52Z","timestamp":1760034472000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/16\/2044"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,14]]},"references-count":52,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["diagnostics15162044"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics15162044","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,14]]}}}