Identifying causal effects of the clinical sentiment of patients' nursing notes on anticipated fall risk stratification

Abstract

Stratifying patients with high risks of falling based on assessment with nursing notes is essential for tailoring anticipated fall prevention strategies. However, the average exposure effects of the clinical sentiment polarity (CSP) of nursing notes on the fall risks of patients are still not well understood. Thus, this study proposes a causal inference framework to identify the exposure effect on patients' anticipated fall risks using electronic nursing notes. The augmented inverse probability weighting model is leveraged to estimate the average exposure effect in a quasi-experiment. The experimental data contains 334,000 words of 2,434 nursing notes from the MIMIC dataset. The results show that the fall risk of exposure to the positive CSP is 0.0054 lower than the control group. The contribution of this paper is three-fold. First, the exposure effect of the CSP in the clinical nursing notes is identified with the causal inference method, which augments the Morse fall scale by analyzing the nursing notes. Second, the Pearson correlations between the exposure and the MFS are sensitive to the cut-off of the clinical sentiment score. By contrast, our results show that their impact on the fall risks in the weighting model can be identified (negative effect) when reducing the bias of covariate imbalance. Compared to the risk factors in the Morse fall scale, the CSP takes a scale of 10 for predicting anticipated fall risks. Third, the CSP factor can be extracted from the sentiment lexicons of nursing notes on the patient's fall risks with information processing methods, which contributes to the clinical decision support of anticipated fall prevention.