## 4 Results
### Error Analysis  
In order to identify the dependencies of the documents classification with their length and their ratio of words present in the GloVe's and Wor2 Vec's dictionaries, we applied a set of statistical tests over two groups of data, the well-classified documents and the miss-classified documents. For calculating the ratio of words present in the dictionary for each document, we count words belonging to the dictionary and divide them by the length of the note. The average word ratio in the dictionary for the whole dataset was \(89.41\pm 4.85\%\). We used a Mann-Whitney-Wilcoxon test, given the nature of the data, where each electronic note is independent one each other. The \(t\)-test was discarded because the length of the documents and words ratio in dictionary did not have a normal distribution. The Mann-Whitney-Wilcoxon (MWW) test is a non-parametric test that contrasts whether two samples come from equidistributed populations.  
For the error analysis, we used the results given by the two pipelines with top performance: (1) the one that included the LR classifier and the average of GloVe vector for document representation, and (2) the one that included the SVM classifier and PCA computed on Wor2 Vec characterization. Figure 3a shows that the distribution of documents that were correctly classified present higher medians than incorrect ones; the MWW test confirms that there are significant differences between both groups with a \(p\)-value of \(2.47\times 10^{-10}\). Additionally, the dictionary word ratio presents a significant difference between documents classified as correct and incorrect (\(p\)-value = \(2.725\times 10^{-16}\)). Similar behavior was observed in pipeline (2) where the documents of a correctly classified document tended to contain more words (\(p\)-value = \(4.25\times 10^{-7}\)) and tended to have a higher dictionary word ratio (\(p\)-value = \(2.2\times 10^{-16}\)).  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**SMM Group**} & \multicolumn{3}{c}{**SVM—Word2 Vec Correction—PCA**} & \multicolumn{3}{c}{**LogReg—GloVe Correction—Average**} \\  & **TN** & **FP** & **FN** & **TP** & **Sens.** & **PPV** & **TN** & **FP** & **FN** & **TP** & **Sens.** & **PPV** \\ \hline WS & 1588 & 228 & 2216 & 4507 & 0.670 & 0.952 & 1594 & 222 & 2322 & 4401 & 0.654 & 0.952 \\ OC & 6715 & 1362 & 194 & 268 & 0.580 & 0.164 & 6672 & 1405 & 180 & 282 & 0.610 & 0.167 \\ HC & 7856 & 316 & 121 & 246 & 0.670 & 0.438 & 7834 & 338 & 119 & 248 & 0.676 & 0.423 \\ MC & 8111 & 77 & 52 & 299 & 0.852 & 0.795 & 8115 & 73 & 43 & 308 & 0.877 & 0.808 \\ HD & 7856 & 375 & 45 & 263 & 0.854 & 0.412 & 7846 & 385 & 51 & 257 & 0.834 & 0.400 \\ OS & 7988 & 317 & 52 & 182 & 0.777 & 0.365 & 7981 & 324 & 50 & 184 & 0.786 & 0.363 \\ PI & 8469 & 20 & 29 & 21 & 0.420 & 0.512 & 8453 & 36 & 30 & 20 & 0.400 & 0.357 \\ NS & 8452 & 43 & 29 & 15 & 0.341 & 0.259 & 8453 & 42 & 30 & 14 & 0.318 & 0.250 \\ \hline \hline \end{tabular}
\end{table}
Table 5: True negative (TN), false positive (FP), false negative (FN), true positive (TP), true positive rate (TPR), and positive predictive value (PPV) for the best models in the test set.