Name
Title | ||
|---|---|---|
Using multiple sentiment dimensions of nursing notes to predict mortality in the intensive care unit |
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/BHI.2018.8333424 | 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI) |
Keywords | Field | DocType |
intensive care unit,30-day mortality prediction,sentiment dimensions features,nursing notes multiple sentiment dimensions,SAPS-II,word frequency method,machine learning methods,ICU,correlation feature selection filter,recursive feature elimination,random forest classifier,receiver operating curve,logistic regression classifier,sentiment analysis | Receiver operating characteristic,Feature selection,Ranking,Word lists by frequency,Sentiment analysis,Computer science,Statistics,Random forest,Standard deviation,Logistic regression | Conference |
ISBN | Citations | PageRank |
978-1-5386-2406-7 | 0 | 0.34 |
References | Authors | |
0 | 2 | |