TY - JOUR
T1 - Latent space representation of electronic health records for clustering dialysis-associated kidney failure subtypes
AU - Onthoni, Djeane Debora
AU - Lin, Ming Yen
AU - Lan, Kuei Yuan
AU - Huang, Tsung Hsien
AU - Lin, Hong Ming
AU - Chiou, Hung Yi
AU - Hsu, Chih Cheng
AU - Chung, Ren Hua
N1 - Publisher Copyright:
© 2024 Elsevier Ltd
PY - 2024/12
Y1 - 2024/12
N2 - Objective: Kidney failure manifests in various forms, from sudden occurrences such as Acute Kidney Injury (AKI) to progressive like Chronic Kidney Disease (CKD). Given its intricate nature, marked by overlapping comorbidities and clinical similarities—including treatment modalities like dialysis—we sought to design and validate an end-to-end framework for clustering kidney failure subtypes. Materials and methods: Our emphasis was on dialysis, utilizing a comprehensive dataset from the UK Biobank (UKB). We transformed raw Electronic Health Record (EHR) data into standardized matrices that incorporate patient demographics, clinical visit data, and the innovative feature of visit time-gaps. This matrix structure was achieved using a unique data cutting method. Latent space transformation was facilitated using a convolution autoencoder (ConvAE) model, which was then subjected to clustering using Principal Component Analysis (PCA) and K-means algorithms. Results: Our transformation model effectively reduced data dimensionality, thereby accelerating computational processes. The derived latent space demonstrated remarkable clustering capacities. Through cluster analysis, two distinct groups were identified: CKD-majority (cluster 1) and a mixed group of non-CKD and some CKD subtypes (cluster 0). Cluster 1 exhibited notably low survival probability, suggesting it predominantly represented severe CKD. In contrast, cluster 0, with substantially higher survival probability, likely to include milder CKD forms and severe AKI. Our end-to-end framework effectively differentiates kidney failure subtypes using the UKB dataset, offering potential for nuanced therapeutic interventions. Conclusions: This innovative approach integrates diverse data sources, providing a holistic understanding of kidney failure, which is imperative for patient management and targeted therapeutic interventions.
AB - Objective: Kidney failure manifests in various forms, from sudden occurrences such as Acute Kidney Injury (AKI) to progressive like Chronic Kidney Disease (CKD). Given its intricate nature, marked by overlapping comorbidities and clinical similarities—including treatment modalities like dialysis—we sought to design and validate an end-to-end framework for clustering kidney failure subtypes. Materials and methods: Our emphasis was on dialysis, utilizing a comprehensive dataset from the UK Biobank (UKB). We transformed raw Electronic Health Record (EHR) data into standardized matrices that incorporate patient demographics, clinical visit data, and the innovative feature of visit time-gaps. This matrix structure was achieved using a unique data cutting method. Latent space transformation was facilitated using a convolution autoencoder (ConvAE) model, which was then subjected to clustering using Principal Component Analysis (PCA) and K-means algorithms. Results: Our transformation model effectively reduced data dimensionality, thereby accelerating computational processes. The derived latent space demonstrated remarkable clustering capacities. Through cluster analysis, two distinct groups were identified: CKD-majority (cluster 1) and a mixed group of non-CKD and some CKD subtypes (cluster 0). Cluster 1 exhibited notably low survival probability, suggesting it predominantly represented severe CKD. In contrast, cluster 0, with substantially higher survival probability, likely to include milder CKD forms and severe AKI. Our end-to-end framework effectively differentiates kidney failure subtypes using the UKB dataset, offering potential for nuanced therapeutic interventions. Conclusions: This innovative approach integrates diverse data sources, providing a holistic understanding of kidney failure, which is imperative for patient management and targeted therapeutic interventions.
KW - Acute kidney injury
KW - Chronic kidney disease
KW - Clustering
KW - Convolutional autoencoder
KW - Electronic health record
KW - Kidney failure
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U2 - 10.1016/j.compbiomed.2024.109243
DO - 10.1016/j.compbiomed.2024.109243
M3 - Article
C2 - 39369548
AN - SCOPUS:85205580914
SN - 0010-4825
VL - 183
JO - Computers in Biology and Medicine
JF - Computers in Biology and Medicine
M1 - 109243
ER -