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- W3021260577 abstract "Objectives: To determine whether time series analysis and Shannon information entropy of facialexpressions predict acute clinical deterioration in patients on general hospital wards.Design: Post-hoc analysis of a prospective observational feasibility study (VIEWS study)Setting: General ward patients in a Community HospitalPatients: Thirty-four patients at risk of clinical deteriorationInterventions: A 3 minute video (153000 frames) for each of the patients enrolled into the VIEWSstudy database was analysed by a trained psychologist for facial expressions measured as ActionUnits (AU) using the Facial Action Coding System (FACS).Measurements and Main Results: 3688 AU were analysed over the 34 three-minute study periods.The AU time variables considered were onset, apex, offset and total time duration. A generalizedlinear regression model and time series analyses were performed. Shannon Information entropy(Hn) and Diversity (Dn) were calculated from the frequency and repertoire of facial expressions.Patients subsequently admitted to critical care displayed a reduced frequency rate (95% CI movingaverage of the mean: 9.5-10.9 vs. 26.1-28.9 in those not admitted), a higher Shannon informationentropy (0.30±0.06 vs. 0.26±0.05, p=0.019) and a prolonged AU reaction time (23.5 vs. 9.4 seconds)compared to patients not admitted to ICU. The number of AU identified per window within thetime-series analysis predicted admission to critical care with an area under the curve (AUC) of 0.88.The AUC for NEWS alone, Hn alone, NEWS plus Hn, and NEWS plus Hn plus Dn were 0.53, 0.75, 0.76and 0.81, respectively.Conclusion: Time-series analysis and Shannon information theory applied to facial AU identified adecreased frequency and a higher information entropy of facial expression in patients at risk ofdeterioration in hospital wards." @default.
- W3021260577 created "2020-05-13" @default.
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- W3021260577 date "2020-04-06" @default.
- W3021260577 modified "2023-09-24" @default.
- W3021260577 title "Do temporal changes in facial expressions help identify patients at risk of deterioration in hospital wards? A post-hoc analysis of the VIEWS study" @default.
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