Proposal of Multi-Connected Hierarchical Text Mining Method for Medical Incident Reports

Abstract

Incident report system is widely employed to prevent medical accidents in hospitals. An incident report is the document described by nurse for an occurrence that might lead to medical accident during working time. It is expected to prevent medical accidents by analyzing incident reports. However, the analysis for incident reports has been statistically done by only using their metadata, e.g. occurred time, category of occurrence, skill of staff and so on. Though this statistical analysis gives us the tendency or classification, it has lost the most important information written in the text parts. This paper proposes a new knowledge extraction method from the text parts of incident reports using metadata and co-occurrence information in the text data. The proposed method can generate a keyword graph with multi-connected hierarchical architecture using text data and metadata, and one of the features of this method is that we can actively analyze what we want to know. This paper applies the proposed method to actual incident reports, and it shows the effectiveness of this method.