## 3 Methodology
### Pattern Extraction Process
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**S.No** & **Extracted Patterns** & **KE-1** & **KE-2** & **KE-3** & **KE-4** & **KE-5** & **Total Score** \\ \hline
1 & “.load()? 10.* & 3 & 3 & 2 & 1 & 2 & 11 \\
2 & “.treatment (should | with | to)* & 2 & 3 & 4 & 4 & 3 & 16 \\
3 & “.initial(?treatment.\" & 2 & 3 & 2 & 3 & 4 & 14 \\
4 & “.to improve.\" & 4 & 4 & 1 & 3 & 5 & 17 \\
5 & “.evidence(?)(?) support.* & 1 & 4 & 2 & 1 & 3 & 11 \\
6 & “.patient(?)” with (disease)* & 3 & 3 & 4 & 5 & 4 & 19 \\
7 & “.should (clude |.curve.\") & 5 & 3 & 3 & 2 & 5 & 18 \\
8 & “.appelling(s red) (toj).* & 2 & 1 & 3 & 2 & 3 & 11 \\
9 & “.can be used.” & 3 & 2 & 2 & 1 & 2 & 10 \\
10 & “.add (remove.\") drug.* & 4 & 4 & 3 & 5 & 5 & 21 \\
11 & “.panel(s)./recommend(ed)? & 4 & 2 & 2 & 1 & 3 & 12 \\
12 & “.less effective.” & 1 & 3 & 4 & 2 & 3 & 13 \\
13 & “.treatment (does not?) need.* & 2 & 3 & 2 & 3 & 3 & 13 \\
14 & “.merardless of* & 3 & 4 & 3 & 3 & 2 & 15 \\
15 & “.meet.\" & 2 & 1 & 2 & 2 & 3 & 10 \\
16 & “.(increase |.choose.\")dose.* & 5 & 4 & 5 & 5 & 4 & 23 \\
17 & “.(recommend(ed)?) treatment.* & 3 & 4 & 3 & 3 & 16 \\
18 & “.(improve(ment)) & 2 & 3 & 3 & 2 & 12 \\
19 & “.(Recommendation/4/+s+.\" & 5 & 5 & 5 & 5 & 5 & 25 \\
20 & “.expert(s)?:“option.\" & 3 & 2 & 3 & 2 & 13 \\
21 & “.(dis)/Continu(e red |ing | action).* & 4 & 3 & 3 & 2 & 4 & 16 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation matrix for nominal group technique (NGT). The key advantage of this approach is its ease of use and comprehensibility for human beings without detailed domain knowledge. However, this approach highly depends on the terms and terminologies of a specific guideline. Therefore, the extracted patterns may not well-perform for all guidelines. To overcome this drawback, we generalized the extracted patterns with the incorporation of two other techniques POS and UMLS patterns for getting a generic solution. The general purpose of POS tagger is to briefly characterize and disambiguate the grammatical category of words in a specific context. It helps to find the similarity and distinction between words. In the proposed method, the POS-based classification is used to generalize the solution for avoiding domain dependency. In this study, the application of the POS tag produced inferior results. Therefore, we used the semi-POS method, which is the combination of POS tags along with clue words. For example, in \". * VB. * drug. *\" \"VB\" is a POS that represents a verb while \"drug\" is a clue word. The list of POS tags, used in the study, is described in Table 5.  
The extracted heuristic patterns shown in Table 4 is transformed into POS patterns as shown in Table 6. We employed the Stanford CoreNLP parser [34] to parse the input \begin{table}
\begin{tabular}{l l} \hline \hline
**S.No** & **Patterns with Salient Terms** \\ \hline
1 & *(give | add | remove) (.*) drug.* \\
2 & *([I i]n) (black | general) (.*) population.* \\
3 & *(recommend(ed)? | better) treatment.* \\
4 & *(increase | decrease).*dose.* \\
5 & *((public)? opinion).* treatment (should | with | to).* \\
6 & *Recommendation /d+/s+:.* \\
7 & *should (include | continue).* \\
8 & *(dis)?continuue(| ed | ing | action) | reach.* goal.* \\
9 & *(regardless of) | (having age).* \\
10 & *(patient(s)? | adult | (population group) )?with (disease).* \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Extracted heuristics patterns with salient terms. \begin{table}
\begin{tabular}{c l l} \hline \hline
**S.No** & **Patterns without Salient Terms** \\ \hline
1 & *(add | remove) (.*) drug.* \\
2 & *(recommend(ed(ed)?) treatment.* \\
3 & *(roimprove.* \\
4 & *(increase | decrease).*dose.* \\
5 & *(treatment (should | with | to).* \\
6 & *Recommendation /d+/s+:.* \\
7 & *should (include | continue).* \\
8 & *(dis)?continuue(| ed | ing | action).* \\
9 & *regardless of.* \\
10 & *(patient(s)? | adult | (population group) )?with (disease).* \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Extracted heuristics patterns without salient terms. sentences to their POS categories.