Predicting neuropsychiatric symptoms of persons with dementia in a day care center using a facial expression recognition system

Abstract

Background: Behavioral and psychological symptoms of dementia (BPSD) affect 90% of persons with dementia (PwD), resulting in various adverse outcomes and aggravating care burdens among their caretakers. This study aimed to explore the potential of artificial intelligence-based facial expression recognition systems (FERS) in predicting BPSDs among PwD. Methods: A hybrid of human labeling and a preconstructed deep learning model was used to differentiate basic facial expressions of individuals to predict the results of Neuropsychiatric Inventory (NPI) assessments by stepwise linear regression (LR), random forest (RF) with importance ranking, and ensemble method (EM) of equal importance, while the accuracy was determined by mean absolute error (MAE) and root-mean-square error (RMSE) methods. Results: Twenty-three PwD from an adult day care center were enrolled with ≥ 11,500 FERS data series and 38 comparative NPI scores. The overall accuracy was 86% on facial expression recognition. Negative facial expressions and variance in emotional switches were important features of BPSDs. A strong positive correlation was identified in each model (EM: r = 0.834, LR: r = 0.821, RF: r = 0.798 by the patientwise method; EM: r = 0.891, LR: r = 0.870, RF: r = 0.886 by the MinimPy method), and EM exhibited the lowest MAE and RMSE. Conclusions: FERS successfully predicted the BPSD of PwD by negative emotions and the variance in emotional switches. This finding enables early detection and management of BPSDs, thus improving the quality of dementia care.

Using AI-Based Technologies to Help Nurses Detect Behavioral Disorders: Narrative Literature Review

“…Publications from 14 countries were identified, with publication dates ranging from 2006 to 2023 ( Figure 1 ). A total of 8 publications addressed acute care settings [ 31 - 38 ], 6 looked at nursing homes [ 39 - 44 ], and 3 examined community care [ 45 - 47 ]. The majority described prospective observational studies published in journals covering geriatrics and psychogeriatrics [ 31 , 33 , 34 , 36 , 46 - 48 ] ( Multimedia Appendix 2 ) [ 31 - 55 ].…”

“…A total of 12 publications reported using machine learning–type AITs, including the facial expression recognition and predictive modeling subtypes [ 31 , 34 - 38 , 43 , 46 , 47 , 49 , 50 , 55 ], 11 publications explored digital health–type AITs, including the wearable technologies and robotic subtypes [ 32 , 39 - 42 , 44 , 45 , 51 - 54 ], and 2 publications examined natural language processing–type AITs [ 33 , 48 ] ( Figure 2 ).…”

“…One publication reported on the use of AITs to predict the stage and progression of dementia [ 31 ], 1 described the detection and measurement of dementia’s clinical symptoms [ 48 ], and 6 examined dementia management [ 39 , 41 , 44 , 51 - 53 ]. As for the BPSD, 5 publications reported using AITs to predict behavior [ 33 , 36 , 37 , 46 , 47 ], 3 described the detection and assessment of clinical symptoms [ 35 , 40 , 49 ], and 5 looked at managing behavior [ 32 , 42 - 44 , 55 ] ( Figure 4 ). In the context of the BPSD, behavior management refers to interventions carried out to identify and act on trigger factors.…”