## 4 Experiment
### Result
#### 4.4.1 Compare VS-GRU with Baselines
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Mortality Task** & **All 4 Tasks** \\ \hline RF-forward & 0.8211 \(\pm\) 0.010 & 0.8428 \(\pm\) 0.019 \\ RF-mean & 0.7601 \(\pm\) 0.004 & 0.7338 \(\pm\) 0.008 \\ RF-zero & 0.7753 \(\pm\) 0.010 & 0.7501 \(\pm\) 0.009 \\ RF-mask-forward & 0.8346 \(\pm\) 0.009 & 0.8397 \(\pm\) 0.014 \\ RF-mask-mean & 0.7628 \(\pm\) 0.011 & 0.7424 \(\pm\) 0.013 \\ RF-mask-forward-m & 0.8375 \(\pm\) 0.005 & 0.8412 \(\pm\) 0.015 \\ RF-mask-forward-m w/o \(\mu\) & 0.8362 \(\pm\) 0.013 & 0.8358 \(\pm\) 0.014 \\ LR-forward & 0.7268 \(\pm\) 0.023 & 0.7909 \(\pm\) 0.007 \\ LR-mean & 0.6484 \(\pm\) 0.017 & 0.6721 \(\pm\) 0.008 \\ LR-zero & 0.7734 \(\pm\) 0.015 & 0.7268 \(\pm\) 0.012 \\ LR-mask-forward & 0.7375 \(\pm\) 0.013 & 0.7838 \(\pm\) 0.016 \\ LR-mask-mean & 0.6833 \(\pm\) 0.018 & 0.6664 \(\pm\) 0.018 \\ LR-mask-forward-m & 0.7456 \(\pm\) 0.016 & 0.7844\(\pm\)0.011 \\ LR-mask-forward-m w/o \(\mu\) & 0.7316 \(\pm\) 0.019 & 0.7881\(\pm\)0.016 \\ LSTM-forward & 0.8206 \(\pm\) 0.018 & 0.8378 \(\pm\) 0.021 \\ LSTM-mean & 0.8034 \(\pm\) 0.013 & 0.7766 \(\pm\) 0.006 \\ LSTM-zero & 0.7759 \(\pm\) 0.016 & 0.8110 \(\pm\) 0.016 \\ LSTM-mask-forward & 0.8230 \(\pm\) 0.019 & 0.8452 \(\pm\) 0.021 [4] \\ LSTM-mask-mean & 0.8062 \(\pm\) 0.017 & 0.8217 \(\pm\) 0.021 \\ GRU-forward & 0.8198 \(\pm\) 0.016 & 0.8442 \(\pm\) 0.017 [5] \\ GRU-mean & 0.8016 \(\pm\) 0.006 & 0.7962 \(\pm\) 0.004 \\ GRU-zero & 0.7929 \(\pm\) 0.020 & 0.8281 \(\pm\) 0.012 \\ GRU-mask-forward & 0.8306 \(\pm\) 0.024 & 0.8477 \(\pm\) 0.015 [2] \\ GRU-mask-mean & 0.8132 \(\pm\) 0.011 & 0.8458 \(\pm\) 0.010 [3] \\ GRU-D [23] & 0.8424 \(\pm\) 0.012 [3] & 0.8370 \(\pm\) 0.012 \\
**VS-GRU** & **0.8502 \(\pm\) 0.010**[1] & 0.8280 \(\pm\) 0.016 \\
**VS-GRU-i** & 0.8388 \(\pm\) 0.017 [5] & 0.8433 \(\pm\) 0.016 \\
**VS-GRU w/o di** & 0.8454 \(\pm\) 0.010 [2] & 0.8405 \(\pm\) 0.015 \\
**VS-GRU-i w/o di** & 0.8387 \(\pm\) 0.016 [4] & **0.8534 \(\pm\) 0.018**[1] \\ \hline \hline \end{tabular}  
* Number 1–5 means the ranking in experimental results.  
\end{table} 
Table 1: Classification performances on PhysioNet. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Mortality Task** & **ICD-9 Code Tasks** \\ \hline RF-forward & 0.8314 \(\pm\) 0.010 & 0.6941 \(\pm\) 0.005 \\ RF-mean & 0.7699 \(\pm\) 0.011 & 0.6494 \(\pm\) 0.003 \\ RF-zero & 0.7926 \(\pm\) 0.005 & 0.6525 \(\pm\) 0.003 \\ RF-mask-forward & 0.8243 \(\pm\) 0.010 & 0.6935 \(\pm\) 0.006 \\ RF-mask-mean & 0.7933 \(\pm\) 0.004 & 0.6515 \(\pm\) 0.003 \\ RF-mask-forward-m & 0.8248 \(\pm\) 0.007 & 0.6978 \(\pm\) 0.004 \\ RF-mask-forward-m w/o \(\mu\) & 0.8247 \(\pm\) 0.009 & 0.6965 \(\pm\) 0.006 \\ LR-forward & 0.7722 \(\pm\) 0.016 & 0.6384 \(\pm\) 0.005 \\ LR-mean & 0.6742 \(\pm\) 0.017 & 0.5830 \(\pm\) 0.003 \\ LR-zero & 0.7564 \(\pm\) 0.019 & 0.6169 \(\pm\) 0.005 \\ LR-mask-forward & 0.7778 \(\pm\) 0.010 & 0.6403 \(\pm\) 0.006 \\ LR-mask-mean & 0.6734 \(\pm\) 0.009 & 0.5842 \(\pm\) 0.001 \\ LR-mask-forward-m & 0.7836 \(\pm\) 0.011 & 0.6512 \(\pm\) 0.003 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification performances on MIMIC-III. To verify our suggestion, we solve the multi-label classification problem as single-label classification, which means we run every binary label independently and average the results. Here, we compare the best model VS-GRU in the binary classification problem with the best model VS-GRU-i w/o di in the multi-label classification problem. The results in Table 3 confirm our suggestion. The dynamic imputation mechanism also fails on MIMIC-III tasks. We can make the conclusion that the weak performance of VS-GRU in solving the multi-task classification problem is because of its lack of parameters to deal with multi-label simultaneously, which is approached in VS-GRU-i. However, if we solve the multi-label classification problem separately, VS-GRU even outperforms VS-GRU-i at the price of time consumption.