Today, medical centers record treatment and clinical care for patients in the form of electronic health records. Most of the necessary clinical care is stored in clinical notes consisting of natural language. The analysis and search of the clinical notes are essential for in electronic health records creates potentially negative consequences for patient care and computational modeling. The growing number of clinical notes clinical processes, including errors due to the omission of important endangering patient's health. information, delays in providing appropriate treatment, and generally Different data mining methods have been proposed to summarize clinical notes, including statistical methods using knowledge bases, the use of cue expressions, and deep learning models. Among the existing challenges is the need for domain specialists to annotate and define comprehensive concepts and relationships between them, create semantic representations of sentences, and enrich the knowledge base. Identifying the phenotypes in clinical notes plays a vital role in resolving this issue and also leads to the identification of the patient group, which is a crucial task in the secondary use of methods proposed so far to solve the problem of identifying disease phenotypes electronic health records for the management of clinical information. The learning approaches require knowledge bases and field experts' intervention to have not been accurate enough to extract related features. Conventional machine not usually able to extract semantic information and grammatical features do feature engineering in clinical notes. On the other hand, deep learning approaches also learn features automatically by deep neural models, which are effectively. In this study, a model consisting of two units is presented, which includes a unit for identifying disease phenotypes based on deep learning to identify the most phenotype using a combination of two knowledge bases, including the output of relevant terms to cardiac and pulmonary phenotypes. A summary unit based on sentences to cardiopulmonary phenotypic abnormalities. the previous unit, as a base of internal knowledge and, the human phenotype ontology, as a base of external knowledge, identifies the most relevant The proposed model extracts more features than the existing methods and provides a better F1score. Also, the phenotype-based summarizing unit, using the phenotypes identified by the deep neural model, without the need for experts in the field and can use content-based embedding automatically extracts topics related to cardiac and pulmonary phenotypes to record the semantic display of sentences without the need for providing challenges of previous methods when using knowledge bases. existing concepts with related terms in knowledge bases for sentence-level analysis of sentences. The phenotype-based summary system could tackle the