Machine learning for identifying severity clusters in Omani patients with systemic lupus erythematosus

الموضوعات

علوم تطبيقية وتكنولوجية

الملخص

Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by the presence of autoantibodies targeting self-antigens, immune complex formation, and immune dysregulation, resulting in damage to multiple organs. The disease can affect the kidneys, skin, blood cells, and nervous system. The natural course of SLE is unpredictable; patients may experience many years of mild symptoms or present with acute, life-threatening conditions. Although advancements in diagnostic techniques and treatment strategies have improved prognosis, there remains a need for deeper understanding and more targeted therapeutic options. Aims: This study aims to: (a) Identify severity clusters among Omani patients with SLE, (b) Detect features associated with disease severity, and (c) Examine the correlation between the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) and Physician Global Assessment (PGA) within each subgroup. Methods: Data were collected from Sultan Qaboos University Hospital (SQUH) and included demographic, clinical, laboratory, and treatment information. The dataset underwent several stages: data cleaning, feature extraction, and exploratory data analysis to understand data types and distributions. Three clustering methods—hierarchical agglomerative clustering, K-Means clustering, and spectral clustering—were applied to group patients. Clustering results were evaluated by examining correlations with SLEDAI and PGA scores. Results: Exploratory data analysis revealed that joint pain was the most common symptom among Omani SLE patients, followed by positive anti-dsDNA antibodies, low complement levels (C3, C4), acute cutaneous lupus (ACL), renal disorders, and hemolytic anemia. Clustering analysis identified two distinct patient groups: a mild and a severe cluster. Patients in the severe cluster had a higher prevalence of renal disorders, hemolytic anemia, positive anti-dsDNA antibodies, and low complement levels. These patients also exhibited cumulative clinical manifestations such as malar rash and proteinuria and required more aggressive treatment, including cyclophosphamide, mycophenolate mofetil, and azathioprine. The mild disease activity cluster was associated mainly with joint pain, low complement levels, and positive anti-dsDNA antibodies, but less severe organ involvement.

روابط وملفات

التعريف والنوع

رقم الوثيقة

ffcd2876-f474-4800-8c99-b9dd92b026ad

رقم العقد

0

نوع الوسائط

Crawler

نوع المحتوى

الرسائل العلمية

صيغة المصدر

رسائل ماجيستير

نوع الملف

pdf text

أسماء الملفات

ffcd2876-f474-4800-8c99-b9dd92b026ad_1.pdf

بيانات النشر

ترجمة العنوان

التعلم الآلي لتحديد مجموعا الشدة في المرضى العمانيين المصابين بالذئبة لحمامية الجهازية (SLE)

ألقاب المؤلفين

[{"name_ar":" Sumaya Khalifa Aamir AL-Sawafiyah ","title_ar":"اعداد","title_en":"Preparation"},{"name_ar":"Aliya AL-Ansari ","title_ar":"اشراف","title_en":"Supervision"},{"name_ar":"Hamza Zidoum","title_ar":"اشراف","title_en":"Supervision"},{"name_ar":"Imran Khan","title_ar":"اشراف","title_en":"Supervision"}]

اللغة

English

المصدر والدورية

اسم المصدر

Machine learning for identifying severity clusters in Omani patients with systemic lupus erythematosus

المحتوى والصفحات

عدد الصفحات

0

كلمات الباحثين

Systemic lupus erythematosus - Autoimmune diseases - Machine learning

إشراف وإعداد

الإشراف

Aliya AL-Ansari , Hamza Zidoum, Imran Khan

الإعداد

Sumaya Khalifa Aamir AL-Sawafiyah

الاقتباسات الببليوغرافية