Identifying Severity Clusters in SLE Patients

Hamza Zidoum*, Sumaya AL-Sawafi, Aliya AL-Ansari, Batool AL-Lawati

*المؤلف المقابل لهذا العمل

نتاج البحث: Conference contribution


Machine learning (ML) has a successful impact in healthcare data mining. We use unsupervised ML methods to extract features and identify subgroups of Systemic Lupus Erythematosus (SLE) patients related to the disease severity. We analyze the similarity between SLE patients within these clusters. Finally, we evaluate the clustering results, using two types of cluster validation, internal cluster validation, and external cluster validation. The clustering analysis results show two separate patients clusters which are mild and severe subgroups. Patients in the severe subgroup have a higher prevalence of the renal disorder, hemolytic anemia, anti-dsDNA anti- body, and low complements (C3, C4). The severe subgroup of patients suffer from malar rash and proteinuria with higher use of cyclophosphamide, mycophenolate mofetil, and azathioprine. The second cluster is mild disease activity, and it is associated with joint pain, low complements (C3, C4), and a positive anti-dsDNA antibody.

اللغة الأصليةEnglish
عنوان منشور المضيفProceedings of the Future Technologies Conference, FTC 2022, Volume 3
المحررونKohei Arai
ناشرSpringer Science and Business Media Deutschland GmbH
عدد الصفحات19
رقم المعيار الدولي للكتب (المطبوع)9783031183430
المعرِّفات الرقمية للأشياء
حالة النشرPublished - 2023
الحدث7th Future Technologies Conference, FTC 2022 - Vancouver, Canada
المدة: أكتوبر ٢٠ ٢٠٢٢أكتوبر ٢١ ٢٠٢٢

سلسلة المنشورات

الاسمLecture Notes in Networks and Systems
مستوى الصوت561 LNNS
رقم المعيار الدولي للدوريات (المطبوع)2367-3370
رقم المعيار الدولي للدوريات (الإلكتروني)2367-3389


Conference7th Future Technologies Conference, FTC 2022

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