A spectral clustering technique for studying post-transplant kidney functions


In this paper, we present a novel method for studying the deterioration of renal functions after kidney transplant. We track the kidney functions of 111 patients for 24 months af- ter the kidney transplant and use the time series data to group the patients into four clusters. We have developed two graph-based algorithms for analyzing the data as a pre- processing step prior to the formation of the clusters. The resultant clusters thus formed are statistically analyzed to determine the socio-demographic and clinical factors that may provide insights into the renal functions after the trans- plants. We also compare the cluster formation against other manifold learning techniques. The quality of the clusters was assessed using the silhouette function. We discuss how our findings can be used for effective intervention strategies. Copyright © 2012 ACM.

Publication Title

IHI'12 - Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium