###### Abstract  
Clinical Practice Guidelines (CPGs) aim to optimize patient care by assisting physicians during the decision-making process. However, guideline adherence is highly affected by its unstructured format and aggregation of background information with disease-specific information. The objective of our study is to extract disease-specific information from CPG for enhancing its adherence ratio. In this research, we propose a semi-automatic mechanism for extracting disease-specific information from CPGs using pattern-matching techniques. We apply supervised and unsupervised machine-learning algorithms on CPG to extract a list of salient terms contributing to distinguishing recommendation sentences (RS) from non-recommendation sentences (NRS). Simultaneously, a group of experts also analyzes the same CPG and extract the initial patterns \"Heuristic Patterns\" using a group decision-making method, nominal group technique (NGT). We provide the list of salient terms to the experts and ask them to refine their extracted patterns. The experts refine patterns considering the provided salient terms. The extracted heuristic patterns depend on specific terms and suffer from the specialization problem due to synonymy and polysemy. Therefore, we generalize the heuristic patterns to part-of-speech (POS) patterns and unified medical language system (UMLS) patterns, which make the proposed method generalize for all types of CPGs. We evaluated the initial extracted patterns on asthma, rhinosinuitis, and hypertension guidelines with the accuracy of 76.92%, 84.63%, and 89.16%, respectively. The accuracy increased to 78.89%, 85.32%, and 92.07% with refined machine-learning assistive patterns, respectively. Our system assists physicians by locating disease-specific information in the CPGs, which enhances the physicians' performance and reduces CPG processing time. Additionally, it is beneficial in CPGs content annotation. recommendation statements identification; guideline processing; pattern extraction; information extraction; clinical text mining +
Footnote †: journal: Medical Image Processing  
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M.H.S.M. and the applicable clinical scenario, which leads to an effective usage rate between 20% and 100% [6]. Some of the common reasons for non-adherence to these guidelines, include, a lack of awareness for the healthcare practitioners, and the difficulty in understanding the large textual content of the CPGs in a limited time, during the clinical practice [6; 7; 8]. One of the possible solutions to this problem is to transform CPGs into a machine-interpretable format and to integrate the knowledge extracted from these, with clinical information systems and Clinical Decision Support Systems (CDSS). This knowledge integration leads to the creation of Guideline-based CDSS, which can provide disease-specific recommendations for optimizing and customizing the patient care. Additionally, machine-interpretable CPGs allow the physicians to save their valuable time, by providing a meaningful abstraction of the contents and disease-specific information, thereby enhancing healthcare delivery. Based on the importance of the provided information, CPG contents can be categorized into two parts. First, the background information, which includes abstract information related to the background and point of view of the authors. Second, the disease-specific information, which elaborates causes, consequences, and actions related to a disease. For instance, the sentence, \"Hypertension remains one of the most important preventable contributors to disease and death.\" represents background information, while \"In the black hypertensive population, including those with diabetes, a calcium channel blocker or thiazide-type diuretic is recommended as initial therapy.\" represents disease-specific information, also known as a recommendation sentence. Therefore, the understandability and classification of CPG contents is an important step, before its transformation to computer interpretable format.