TY - GEN
T1 - Identifying Severity Clusters in SLE Patients
AU - Zidoum, Hamza
AU - AL-Sawafi, Sumaya
AU - AL-Ansari, Aliya
AU - AL-Lawati, Batool
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Biomedical informatics
KW - Clustering
KW - Data analytics
KW - Healthcare
KW - Systemic Lupus Erythematosus (SLE)
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UR - https://www.mendeley.com/catalogue/9694dfad-d912-3ebe-a304-d6b8eb36214a/
U2 - 10.1007/978-3-031-18344-7_28
DO - 10.1007/978-3-031-18344-7_28
M3 - Conference contribution
AN - SCOPUS:85142080731
SN - 9783031183430
T3 - Lecture Notes in Networks and Systems
SP - 413
EP - 431
BT - Proceedings of the Future Technologies Conference, FTC 2022, Volume 3
A2 - Arai, Kohei
PB - Springer Science and Business Media Deutschland GmbH
T2 - 7th Future Technologies Conference, FTC 2022
Y2 - 20 October 2022 through 21 October 2022
ER -