Pattern-based Mining in Electronic Health Records for Complex Clinical Process Analysis

This paper presents the application of text mining methods to the texts in electronic health records (EHR). It is shown in an experimental study how to raise the data possibility to reflect the real medical processes for process modeling tasks. The method is based on the patterns identified in the analysis of medical databases with the physician assistant. EHR is characterized by the gap between common semantic structure and syntactic structure what is important for complex processes modeling. This study aimed at the solution of the problem of knowledge retrieval from EHR by identifying the specifics of their semantic structure and the development of algorithms for interpretation of medical records using the text mining. The medical tests description, surgery protocols, and other medical documents contain many extremely important items for the process analysis. By automating the retrieval of significant data from EHR can be also used for knowledge bases filling. Moreover, the proposed method is developed during the study of actual Russian language medical data of Acute coronary syndrome (ACS) patients from the current specialized medical center which also valuable. The efficiency of this method is demonstrated in the course of correlation analysis of comorbidities on the treatment duration of ACS and in the case of extracted data using to develop process models with complexity metrics at the control-flow perspective of process mining techniques.