Table 3 Generalization performance on T1D/T2D benchmark data

From: Deep representation learning for clustering longitudinal survival data from electronic health records

Method

ACC

NMI

ARI

CI

k-means+Cox PH

0.40 ± 0.01*(0.008)

0.06 ± 0.01*(0.008)

0.09 ± 0.01*(0.008)

0.51 ± 0.01*(0.008)

SSC

0.40 ± 0.01*(0.008)

0.06 ± 0.02*(0.008)

0.087 ± 0.009*(0.008)

 

SCA

0.53 ± 0.03*(0.008)

0.005 ± 0.006*(0.008)

0.02 ± 0.02*(0.008)

0.60 ± 0.02*(0.008)

DSM

0.498 ± 0.007*(0.011)

0.0003 ± 0.0005*(0.011)

0.003 ± 0.009*(0.011)

0.37 ± 0.02*(0.008)

RDSM

0.503 ± 0.003*(0.011)

0.002 ± 0.002*(0.011)

0.006 ± 0.008*(0.011)

0.63 ± 0.13*(0.008)

VaDeSC-MLP

0.71 ± 0.09*(0.016)

0.15 ± 0.09(0.222)

0.13 ± 0.14(0.548)

0.72 ± 0.07(0.690)

VaDeSC-EHR_nosurv

0.64 ± 0.06*(0.008)

0.06 ± 0.05*(0.008)

0.08 ± 0.03*(0.008)

 

VaDeSC-EHR_relage

0.81 ± 0.02(0.458)

0.24 ± 0.04(0.458)

0.23 ± 0.04(0.458)

0.71 ± 0.01*(0.047)

VaDeSC-EHR

0.81 ± 0.02

0.24 ± 0.04

0.23 ± 0.04

0.72 ± 0.01

  1. Comparison between VaDeSC-EHR and the other methods used for clustering longitudinal survival data, in terms of balanced accuracy (ACC), normalized mutual information (NMI), adjusted Rand index (ARI), and concordance index (CI). Reported ± is one standard deviation, and the significance of any difference (p-value < 0.05) between VaDeSC-EHR and the other methods is indicated by an asterisk. Significance is based on a two-sided Mann–Whitney U-test. Detailed p-values are shown in the brackets.