## 4 Results  
### Dataset Characterization  
We identified 22,937 patients from the EHRs, most of them with more than one progress note. We built a dataset by grouping all the progress notes per episode into one document. A total of 43,529 documents were built. We assigned the ICD-10 discharge code to each document. Figure 11 shows the distribution of ICD-10 codes in our dataset by its chapters.  
These documents have a mean of 4.15 progress notes. Table 2 presents the number of documents per class and the mean progress notes in such documents. Due to the low frequency of pulmonary sepsis after validating with physicians, we merged this class into non-obstetrics sepsis for analysis.  
Despite the query being targeted for women that have a delivery in the study time-frame, we found ICD-10 codes associated to the episodes in almost all chapters of ICD-10. As expected, the most frequent chapter was the XV, corresponding to a pregnancy, child birth, and Puerperium. In this chapter, the most frequent code found was the single spontaneous delivery followed by single delivery by cesarean section and false labor. The second most frequent chapter was XXI, which is related to the factors influencing health status and contact with health services. In this chapter, the most frequent code is related to the supervision of normal pregnancy. The third most frequent chapter was XVIII, which is related to symptoms, signs, and abnormal clinical and laboratory findings; the most frequent codes in this chapter were pelvic pain and headache. Lastly, the fourth most frequent chapter was the XIV, associated to diseases of the genitourinary system, where the most frequent code were other disorders of the urinary system.  
We analyzed the number of tokens for each document at two moments. First, after removing the common stop words, and second, after the elimination of customized stop words identified by the health informatician of the research team. Additionally, MC and NS were the classes most affected by the removal of stop words. Without common stop words the average number of tokens per document is 431. After removing the customized stop words, the average tokens per documents decreases to 64. Figure 1 shows the distribution of the number of tokens differentiated by SMM classes. Importantly, the documents left empty after removing stop words were discarded.  
\begin{table}
\begin{tabular}{c l c c c} \hline \hline
**Acronym** & **SMM Group** & \begin{tabular}{c} **Number of** \\ **Documents** \\ \end{tabular} & \
\begin{tabular}{c} **Proportion of** \\ **Documents** \\ \end{tabular} &
\begin{tabular}{c} **Mean of Progress Notes** \\ \end{tabular} \\ \hline WS & Without SMM & 34,117 & 0.784 & \(4.01\pm 6.28\) \\ OC & Other causes & 2345 & 0.054 & \(5.54\pm 9.39\) \\ HC & Hemorrhagic complications & 1922 & 0.044 & \(3.19\pm 4.90\) \\ MC & Miscarriage complications & 1919 & 0.044 & \(2.39\pm 3.96\) \\ HD & Hypertensive disorders & 1538 & 0.035 & \(6.52\pm 9.37\) \\ OS & Obstetric sepsis & 1187 & 0.027 & \(5.99\pm 7.38\) \\ PI & Complication of pre-existing illness & 249 & 0.006 & \(6.97\pm 8.91\) \\ NS & Non-Obstetrics sepsis & 228 & 0.005 & \(4.69\pm 14.50\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of documents per SMM class and mean progress notes.  
Figure 1: Tokens per document with and without stop words.  
When compared to the GloVe and Word2 Vec vocabularies, the percentage of typos and misspelled words in our entire dataset was between 10.85% and 14.13%. Also, the average percentage of missing words per document was 10.58% in GloVe and 13.77% in Word2 Vec. We consider that these spelling errors occur due to the time assigned to physicians to fill out the EHR and the lack of an autocorrelation functionality in the EHR system. Our numbers were similar with the results of Ruch [34] in follow-up notes.