## 2 Related Work  
CPGs have acted as a formal resource for identification and adaptation of best practices in the medical domain, since the late 1970s [22]. However, the transformation of theory, in the form of textual CPGs, into common clinical practice, is only possible, when it is transformed into a machine-interpretable model. Many approaches have been proposed to achieve this transformation, especially focusing on representing and executing knowledge from CPGs over patient-specific clinical data. Some of these approaches include document-centric models, probabilistic models, decision trees, and task network models, which can be used to represent guidelines in an appropriate format [23]. Still, the current solutions for automatic conversion of CPGs into computer interpretable format, have many limitations. The primary hurdle here, is the difficulty in accurately recognizing and extracting, recommendation sentences from the textual content, since most of the recommendation sentences are not written in the \(IF<condition>THEN<action>\) format. Therefore, a technique is required that can extract recommendation sentences from CPG text. Some of the approaches relevant to the proposed approach are described as follows. [PERSON] et al. [24] developed a methodology for the formalization of CPGs using linguistic patterns and predefined templates. The linguistic templates were generated from the domain ontology. Their proposed methodology performed activities including, pattern extraction, selection of core patterns from extracted patterns for the generation of an executable model, and finally the model evaluation. This methodology produced reusable guidelines blocks/templates for authoring and formalizing CPGs. However, this approach needs a customized domain ontology for mapping the concepts, while generating the template. [PERSON] et al. [4] proposed a rule-based method using a combination of linguistic and semantic information of UMLS. The authors hypothesized that each CPG statement has an associated, domain-dependent linguistic and semantic pattern. They proposed a weighting coefficient (relevance rate) to extract the relevant statements by identifying the condition-action combination of terms. In this manner, their proposed model can determine the relevancy of each statement towards the clinical workflow. The authors used one guideline for identifying and extracting 12 \"if\" and 4 \"should\" statements. They found that rules of type \"if\" have a better chance of detection than the type \"should\". [PERSON] et al. [25] designed supervised learning models using ZeroR, Naive Bayes, J48, and Random Forest for the classification of CPG statements. Using three annotated guidelines(Hypertension, Chapter 4 of asthma, and rhinosinusitis), these models were trained to classify each CPG statement into no condition (NC), condition-action (CA), or condition consequence (CC). Additionally, Part-of-Speech (POS) tagging was used to remove domain dependency constraints and recommendation statements were identified by using modifiers and regular expressions. The most commonly used modifiers were \"if\", \"in\", \"to\", \"for\", \"when\", and \"which\". The identified recommendation statements were then transformed into \"if condition then consequences\" format for rule generation in later stages. The authors' used models were one shot models, which require retraining each time when a change occurs in the training dataset. [PERSON] et al. [26] proposed a framework for active dissemination of and automatic knowledge extraction from, CPGs. Their proposed framework automates some of the activities to reduce manual efforts. The framework follows a multi-step approach and uses an unstructured information management architecture (UIMA) for identifying medical concepts. The authors achieved this by performing several information processing activities such as XML parsing, text cleansing, medical concept tagging, medical tags disambiguation, clinical context pattern detection, clinical context filtering, and clinical context mapping.