## 3 Results  
The results of the experimental evaluation can be seen in Table 4, and the optimal hyperparameters have been included in Table 1 in Appendix A for completeness. It can be seen that the best prediction was obtained by combining Document Embeddings with a Recurrent Neural Network, closely followed by Binary Bag of Words with a Recurrent Neural Network and Document Embeddings with a Support Vector Machine. From the perspective of the different representations, the weighting scheme of the Bag-of-words approach did not result in a clear difference in performance between different models, and neither was there a strong difference in applying the text embeddings on the word level or the document level seen. Overall however, text embeddings resulted in better performance than the bag-of-words approaches. Of the classical models, the Naive Bayes algorithm, which despite its simplicity, often yield good results in text classification, is in this case, it was not among the top performing algorithms. Decision Trees were able to perform slightly better, but also could not match the performance of the other algorithms. A possible explanation for this is that these models were relatively simple, and they were not able to find the relatively complex patterns that were needed to accurately assess violence risk. The Neural Network algorithm showed better results, especially in combination with the text embeddings, and a clear difference between Bag-of-words representations and Text embedding representations could be seen. The Support Vector Machine finally was able to predict violence incidents in the first 30 of admission days best of the four classical models, with just a marginal difference in performance over different representations of text. For the two Deep Learning models, it can be seen that, especially the RNN, outperformed several of the classical models. RNNs outperformed CNNs for this classification task as well, with a similar margin over different text representations. The AUC scores were however among the lowest when Word Embeddings were used for representing text, while these did not cause a decrease in performance for the classical models, suggesting that they did contain the information that was needed to assess the violence risk. Despite the fact that a sequence of Word Embeddings is a richer representation than the average over these Word Embeddings, the fact that the length of the input sequence was no longer \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Bag-of-Words** & **Bag-of-Words** & \multirow{2}{*}{**Word Embeddings**} & **Document** \\  & **Binary** & **tf-idf** & & **Embeddings** \\ \hline RNN [1] & 0.771 \(\pm\) 0.018 b & 0.753 \(\pm\) 0.031 & 0.654 \(\pm\) 0.043 & 0.788 \(\pm\) 0.018 a,b \\ CNN [2] & 0.729 \(\pm\) 0.030 & 0.716 \(\pm\) 0.038 & 0.684 \(\pm\) 0.038 & 0.763 \(\pm\) 0.024 a \\ NN [3] & 0.727 \(\pm\) 0.033 & 0.717 \(\pm\) 0.038 & 0.751 \(\pm\) 0.036 a & 0.745 \(\pm\) 0.022 \\ NB [4] & 0.686 \(\pm\) 0.026 & 0.704 \(\pm\) 0.034 a & 0.700 \(\pm\) 0.051 & 0.692 \(\pm\) 0.046 \\ SVM [5] & 0.759 \(\pm\) 0.040 & 0.756 \(\pm\) 0.036 b & 0.764 \(\pm\) 0.024 b & 0.770 \(\pm\) 0.029 a \\ DT [6] & 0.727 \(\pm\) 0.018 a & 0.719 \(\pm\) 0.041 & 0.685 \(\pm\) 0.041 & 0.665 \(\pm\) 0.035 \\ \hline \hline \end{tabular}  
* Recurrent Neural Network; [2] Convolutional Neural Network; [3] Neural Network; [4] Naive Bayes; [5] Support Vector Machine; [6] Decision Tree.  
\end{table} 
Table 4: The performance for optimal hyperparameter values for each of the representations combined with the models, based on a 5-fold stratified cross validation. The performance is measured in AUC, along with its standard deviation. The best performance over different models is marked with an a, the best performance over representations with a b.  
proportional to the number of instances might provide difficulties in practice. In this case, given the computational resources and dataset size, the classical models were able to handle the condensed word embedding averages better.