Supporting the use of standardized nursing terminologies with automatic subject heading prediction: a comparison of sentence-level text classification methods

OBJECTIVE: This study focuses on the task of automatically assigning standardized (topical) subject headings to free-text sentences in clinical nursing notes. The underlying motivation is to support nurses when they document patient care by developing a computer system that can assist in incorporating suitable subject headings that reflect the documented topics. Central in this study is performance evaluation of several text classification methods to assess the feasibility of developing such a system. MATERIALS AND METHODS: Seven text classification methods are evaluated using a corpus of approximately 0.5 million nursing notes (5.5 million sentences) with 676 unique headings extracted from a Finnish university hospital. Several of these methods are based on artificial neural networks. Evaluation is first done in an automatic manner for all methods, then a manual error analysis is done on a sample. RESULTS: We find that a method based on a bidirectional long short-term memory network performs best with an average recall of 0.5435 when allowed to suggest 1 subject heading per sentence and 0.8954 when allowed to suggest 10 subject headings per sentence. However, other methods achieve comparable results. The manual analysis indicates that the predictions are better than what the automatic evaluation suggests. CONCLUSIONS: The results indicate that several of the tested methods perform well in suggesting the most appropriate subject headings on sentence level. Thus, we find it feasible to develop a text classification system that can support the use of standardized terminologies and save nurses time and effort on care documentation.