Extraction and representation of nursing diagnosis for assisted assessment and affective analysis

Early detection of changes in patient's condition and making an accurate nursing diagnosis are crucial in clinical practice. Current protocol in nursing diagnosis highly depends on caregiver's experience. Demand on processing a large number of nursing documents and transferring expertise has become an important issue. This paper applies computational linguistics approach to extract knowledge and structural model of diagnosis for assisted assessment and change detection of affective expression. A corpus with peer-reviewed case reports and nursing documents were collected for analysis. The evaluation results show that narrative texts with inherent structure and emotional information can be systematically extracted.