The progressive digitization of medical records has resulted in the accumulation of large amounts of data. Electronic medical data include structured numerical data and unstructured text data. Although text-based medical record processing has been researched, few studies contribute to medical practice. The analysis of unstructured text data can improve medical processes. Hence, this study presents a clustering approach for detecting typical patient's condition from text-based medical record of clinical pathway. In this approach, the sentences in a cluster are merged to generate a "sentence graph" of the cluster after classified feature word by Louvain method. An analysis of real text-based medical records indicates that sentence graphs can represent the medical treatment and patient's condition in a medical process. This method could help the standardization of text-based medical records and the recognition of feature medical processes for improving medical treatment.