## 5 Results
### Classification Task
This result suggests that for this task, the structured disentanglement of static, trend, and event components in our model provides a more effective and powerful representation than the single, mixed hidden state learned by a standard recurrent neural network. Although tree-based models like Random Forest are highly efficient on this dataset, the unique advantage of CoTD-VAE lies in its generative and interpretable framework, which offers deeper insights beyond a simple classification output. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Metric**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Walk**} & \multirow{2}{*}{**Walk**} & \multirow{2}{*}{**Walk**} & \multirow{2}{*}{**Sit**} & \multirow{2}{*}{**Stand**} & \multirow{2}{*}{**Lay**} \\  & & & **Up** & & & \\ \hline \multirow{6}{*}{F1 Score} & LSTM-AE & 0.893 & **0.881** & 0.947 & 0.782 & 0.807 & **1.000** \\  & Transformer-AE & 0.831 & 0.836 & 0.882 & 0.791 & 0.813 & 0.999 \\  & \(\beta\)-VAE & **0.909** & 0.845 & 0.849 & 0.794 & 0.826 & 0.973 \\  & CVAE & 0.759 & 0.811 & 0.684 & **0.984** & **0.942** & 0.983 \\  & **CoTD-VAE** & 0.901 & 0.865 & **0.962** & 0.832 & 0.857 & **1.000** \\ \hline \multirow{6}{*}{Precision} & LSTM-AE & 0.860 & **0.987** & 0.899 & 0.790 & 0.797 & **1.000** \\  & Transformer-AE & 0.834 & 0.832 & 0.874 & 0.793 & 0.819 & 0.998 \\  & \(\beta\)-VAE & **0.896** & 0.861 & 0.820 & 0.824 & 0.802 & 0.998 \\  & CVAE & 0.770 & 0.801 & 0.765 & **0.978** & **0.895** & 0.968 \\  & **CoTD-VAE** & 0.888 & 0.869 & **0.962** & 0.853 & 0.846 & **1.000** \\ \hline \multirow{6}{*}{Recall} & LSTM-AE & **0.929** & 0.796 & **1.000** & 0.774 & 0.818 & **1.000** \\  & Transformer-AE & 0.829 & 0.841 & 0.890 & 0.788 & 0.806 & **1.000** \\ \cline{1-1}  & \(\beta\)-VAE & 0.921 & 0.830 & 0.881 & 0.766 & 0.852 & 0.950 \\ \cline{1-1}  & CVAE & 0.748 & 0.822 & 0.619 & **0.990** & **0.994** & 0.998 \\ \cline{1-1}  & **CoTD-VAE** & 0.913 & **0.860** & 0.962 & 0.813 & 0.868 & **1.000** \\ \hline \hline \end{tabular} Activity abbreviations: Walk (Walking), Walk Up (Walking Upstairs), Walk Down (Walking Downstairs), Sit (Sitting), Stand (Standing), Lay (Laying).  
\end{table} 
Table 2: Aggregate performance of models on the classification task. \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**Taxon-Averaged**} & \multirow{2}{*}{**Macro-Averaged**} & \multirow{2}{*}{**Macro-Averaged**} \\  & & & **F1** & **Precision** & **Recall** \\ \hline LSTM-AE & 0.8850 & 0.8851 & 0.8888 & 0.8862 \\ Transformer-AE & 0.8599 & 0.8586 & 0.8582 & 0.8591 \\ \(\beta\)-VAE & 0.8677 & 0.8661 & 0.8670 & 0.8666 \\ CVAE & 0.8717 & 0.8605 & 0.8627 & 0.8618 \\
**CoTD-VAE** & **0.9026** & **0.9027** & **0.9030** & **0.9027** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: F1 Score, Precision, and Recall for each activity category. Figure 4 presents a detailed performance comparison on the MIMIC-IV thrombus prediction task. The bar charts display the F1-Score and AUC for each model, providing a clear quantitative comparison of their predictive performance. As illustrated, CoTD-VAE demonstrates the best overall performance in this task.