Abstract:
Unstructured clinical data such as nursing notes or discharge summaries are seldom used to predict clinical outcomes, despite containing a lot of information. This study ...Show MoreMetadata
Abstract:
Unstructured clinical data such as nursing notes or discharge summaries are seldom used to predict clinical outcomes, despite containing a lot of information. This study examined several sentiment dimensions of nursing notes for their contributions to 30-day mortality prediction, in the presence of a known predictor of 30-day mortality (SAPS-II). Sentiment dimensions were extracted using a combination of word frequency and machine learning methods. Gender and type of intensive care unit (ICU) were also included as candidate features. The sentiment dimensions are then ranked via a correlation feature selection filter and a recursive feature elimination. SAPS-II was consistently ranked as the best predictor. With a random forest classifier, the predictive performance was significantly improved with sentiment dimensions features (p-value <;0.05) (mean [standard deviation] area under the receiver operating curve with sentiment dimensions: 0.827 [0.011]; without sentiment dimensions: 0.572 [0.010]). Similar improvement was also observed with a logistic regression classifier (p-value <;0.05) (with sentiment dimensions: 0.824 [0.012]; without sentiment dimensions: 0.785 [0.013]). Improvements to mortality prediction is possible by including sentiment analysis.
Date of Conference: 04-07 March 2018
Date Added to IEEE Xplore: 09 April 2018
ISBN Information: