Automatic generation of nursing narratives from entity-attribute-value triplet for electronic nursing records system

Yul Ha Min, Hyeoun-Ae Park, Joo Yun Lee, Soo Jung Jo, Eunjoo Jeon, Namsoo Byeon, Seung Yong Choi, Eunja Chung
Studies in Health Technology and Informatics 2014, 201: 452-60
The aim of this study is to develop and evaluate a natural language generation system to populate nursing narratives using detailed clinical models. Semantic, contextual, and syntactical knowledges were extracted. A natural language generation system linking these knowledges was developed. The quality of generated nursing narratives was evaluated by the three nurse experts using a five-point rating scale. With 82 detailed clinical models, in total 66,888 nursing narratives in four different types of statement were generated. The mean scores for overall quality was 4.66, for content 4.60, for grammaticality 4.40, for writing style 4.13, and for correctness 4.60. The system developed in this study generated nursing narratives with different levels of granularity. The generated nursing narratives can improve semantic interoperability of nursing data documented in nursing records.

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