AI and Neurological Rare & Neuroimmune Diseases ── Myasthenia gravis ── 2026-10-14

Diagram of an explainable machine learning flow for predicting ICU admission in 319 hospitalized patients with myasthenia gravis. Clinical variables and systemic inflammation markers feed four models with prespecified thymoma and age-at-onset subgroups, trained with nested 3-fold validation; XGBoost performed best with an AUC of 0.94. SHAP shows main predictors and phenotype-specific patterns, and a web calculator supports risk stratification. — Image abstract — the whole article on one page (click to enlarge)

1 Myasthenia gravis papers. Lead: Explainable Machine Learning Model for Personalized Assessment of the Need for Intensive Care in Patients with Myasthenia Gravis Based on Phenotype and Inflammatory Markers.

Journals covered and how papers are chosen: see the index

Myasthenia gravis: treatment, trials and AI

Explainable Machine Learning Model for Personalized Assessment of the Need for Intensive Care in Patients with Myasthenia Gravis Based on Phenotype and Inflammatory Markers.
QJM : monthly journal of the Association of Physicians · 2026-10-12 · Evidence: Retrospective observational study
Abstract
BACKGROUND: Early identification of myasthenia gravis (MG) severe exacerbations that require intensive care unit (ICU) admission remains challenging due disease highly heterogeneous. Existing models seldom incorporate phenotype stratification, limiting interpretability and clinical utility. AIM: This study aimed to develop an explainable machine learning (ML) model to assess phenotype-specific risk factors and predict ICU admission for patients with MG. DESIGN AND METHODS: This retrospective study included 319 hospitalized patients with MG. We utilized clinical variables and systemic inflammation markers for ML modeling. Subgroups were prespecified by thymoma status and age at onset. Primary outcome was need for intensive care during the hospitalization. Model performance was assessed. SHapley Additive exPlanations (SHAP) was employed for interpretation of global and individual features. RESULTS: Of 319 admissions, 33 (10.3%) required ICU care. Four ML models were trained with nested 3-fold validation. eXtreme Gradient Boosting performed best (AUC: 0.94). SHAP ranked MGFA clinical classification, infection during admission, body mass index, and inflammatory indices as principal predictors. Subgroup analyses revealed phenotype-specific patterns, including opposite SHAP directions for inflammatory markers between thymoma and non-thymoma groups and distinct rankings between different age of onset groups. SHAP-based personalized explanations revealed patient-specific variations in risk ranking and directionality of predictive features. A web-based calculator was developed to support real-time clinical implementation of the model, potentially allowing for risk stratification. CONCLUSIONS: An explainable machine learning framework that integrates clinical features with systemic inflammation indices reliably predicts ICU admission in MG and reveals phenotype-specific risk patterns, enabling personalized risk stratification in MG management.
Journal IF-equivalent: 0.5 (OpenAlex 2-year mean citedness, value as of 2026-10-13, retrieved 2026-10-14; not the official Clarivate IF)
Reference: Chang CC, Lin KY, Yeh JH, Chiu HC, Liu TC, Lu CJ. Explainable Machine Learning Model for Personalized Assessment of the Need for Intensive Care in Patients with Myasthenia Gravis Based on Phenotype and Inflammatory Markers. QJM: An International Journal of Medicine. 2026 Oct 12 [Epub ahead of print]. doi:10.1093/qjmed/hcag289.
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