Sentiment in nursing notes as an indicator of out-of-hospital mortality in intensive care patients

Abstract

BackgroundNursing notes have not been widely used in prediction models for clinical outcomes, despite containing rich information. Advances in natural language processing have made it possible to extract information from large scale unstructured data like nursing notes. This study extracted the sentiment—impressions and attitudes—of nurses, and examined how sentiment relates to 30-day mortality and survival.MethodsThis study applied a sentiment analysis algorithm to nursing notes extracted from MIMIC-III, a public intensive care unit (ICU) database. A multiple logistic regression model was fitted to the data to correlate measured sentiment with 30-day mortality while controlling for gender, type of ICU, and SAPS-II score. The association between measured sentiment and 30-day mortality was further examined in assessing the predictive performance of sentiment score as a feature in a classifier, and in a survival analysis for different levels of measured sentiment.ResultsNursing notes from 27,477 ICU patients, with an overall 30-day mortality of 11.02%, were extracted. In the presence of known predictors of 30-day mortality, mean sentiment polarity was a highly significant predictor in a multiple logistic regression model (Adjusted OR = 0.4626, p < 0.001, 95% CI: [0.4244, 0.5041]) and led to improved predictive accuracy (AUROC = 0.8189 versus 0.8092, 95% BCI of difference: [0.0070, 0.0126]). The Kaplan Meier survival curves showed that mean sentiment polarity quartiles are positively correlated with patient survival (log-rank test: p < 0.001).ConclusionsThis study showed that quantitative measures of unstructured clinical notes, such as sentiment of clinicians, correlate with 30-day mortality and survival, thus can also serve as a predictor of patient outcomes in the ICU. Therefore, further research is warranted to study and make use of the wealth of data that clinical notes have to offer.

Identifying Urinary Tract Infection-Related Information in Home Care Nursing Notes

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Construct validity of six sentiment analysis methods in the text of encounter notes of patients with critical illness

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Natural Language Word-Embeddings as a glimpse into healthcare at the End Of Life

“…This study therefore has an inverted design to a previous Sentiment Analysis study of nursing notes from the MIMIC-III public ICU dataset which found a relationship between such 'sentiment' with survival 14 ; the 'sentiment' was calculated using a rules-based semantic analysis tool (TextBlob 15 ) designed for generic non-clinical text which assigns a positive or negative 'sentiment' score to a piece of text based on the adjectives, verbs and adverbs used in the text 16,17 . In the current study, both an a priori approach and an unsupervised clustering approach were used showing clear associations with the 'ground truth' of mortality.…”