Revealing posturographic profile of patients with Parkinsonian syndromes through a novel hypothesis testing framework based on machine learning

Fall risk classification with posturographic parameters in community-dwelling older adults: a machine learning and explainable artificial intelligence approach

BACKGROUND

Computerized posturography obtained in standing conditions has been applied to classify fall risk for older adults or disease groups. Combining machine learning (ML) approaches is superior to traditional regression analysis for its ability to handle complex data regarding its characteristics of being high-dimensional, non-linear, and highly correlated. The study goal was to use ML algorithms to classify fall risks in community-dwelling older adults with the aid of an explainable artificial intelligence (XAI) approach to increase interpretability.

METHODS

A total of 215 participants were included for analysis. The input information included personal metrics and posturographic parameters obtained from a tracker-based posturography of four standing postures. Two classification criteria were used: with a previous history of falls and the timed-up-and-go (TUG) test. We used three meta-heuristic methods for feature selection to handle the large numbers of parameters and improve efficacy, and the SHapley Additive exPlanations (SHAP) method was used to display the weights of the selected features on the model.

RESULTS

The results showed that posturographic parameters could classify the participants with TUG scores higher or lower than 10 s but were less effective in classifying fall risk according to previous fall history. Feature selections improved the accuracy with the TUG as the classification label, and the Slime Mould Algorithm had the best performance (accuracy: 0.72 to 0.77, area under the curve: 0.80 to 0.90). In contrast, feature selection did not improve the model performance significantly with the previous fall history as a classification label. The SHAP values also helped to display the importance of different features in the model.

CONCLUSION

Posturographic parameters in standing can be used to classify fall risks with high accuracy based on the TUG scores in community-dwelling older adults. Using feature selection improves the model's performance. The results highlight the potential utility of ML algorithms and XAI to provide guidance for developing more robust and accurate fall classification models. Trial registration Not applicable.

Preventing falls: the use of machine learning for the prediction of future falls in individuals without history of fall

Nowadays, it becomes of paramount societal importance to support many frail-prone groups in our society (elderly, patients with neurodegenerative diseases, etc.) to remain socially and physically active, maintain their quality of life, and avoid their loss of autonomy. Once older people enter the prefrail stage, they are already likely to experience falls whose consequences may accelerate the deterioration of their quality of life (injuries, fear of falling, reduction of physical activity). In that context, detecting frailty and high risk of fall at an early stage is the first line of defense against the detrimental consequences of fall. The second line of defense would be to develop original protocols to detect future fallers before any fall occur. This paper briefly summarizes the current advancements and perspectives that may arise from the combination of affordable and easy-to-use non-wearable systems (force platforms, 3D tracking motion systems), wearable systems (accelerometers, gyroscopes, inertial measurement units-IMUs) with appropriate machine learning analytics, as well as the efforts to address these challenges.

Artificial intelligence for falls management in older adult care: A scoping review of nurses' role

AIM

This study aims to synthesize evidence on nurses' involvement in artificial intelligence research for managing falls in older adults.

BACKGROUND

Artificial intelligence techniques are used to analyse health datasets to aid clinical decision making, patient care and service delivery but nurses' involvement in this area of research for managing falls in older adults remains unknown.

EVALUATION

A scoping review was conducted. CINAHL, the Cochrane Library, Embase, MEDLI and PubMed were searched. Results were screened against inclusion criteria. Relevant data were extracted, and studies summarized using a descriptive approach.

KEY ISSUES

The evidence shows many artificial intelligence techniques, particularly machine learning, are used to identify falls risk factors and build predictive models that could help prevent falls in older adults, with nurses leading and participating in this research.

CONCLUSION

Further rigorous experimental research is needed to determine the effectiveness of algorithms in predicting aspects of falls in older adults and how to implement artificial intelligence tools in gerontological nursing practice.

IMPLICATIONS FOR NURSING MANAGEMENT

Nurses should pursue interdisciplinary collaborations and educational opportunities in artificial intelligence, so they can actively contribute to research on falls management. Nurses should facilitate the collection of digital falls datasets to support this emerging research agenda and the care of older adults.

A review of center of pressure (COP) variables to quantify standing balance in elderly people: Algorithms and open-access code

Postural control is often quantified by recording the trajectory of the center of pressure (COP)-also called stabilogram-during human quiet standing. This quantification has many important applications, such as the early detection of balance degradation to prevent falls, a crucial task whose relevance increases with the aging of the population. Due to the complexity of the quantification process, the analyses of sway patterns have been performed empirically using a number of variables, such as ellipse confidence area or mean velocity. This study reviews and compares a wide range of state-of-the-art variables that are used to assess the risk of fall in elderly from a stabilogram. When appropriate, we discuss the hypothesis and mathematical assumptions that underlie these variables, and we propose a reproducible method to compute each of them. Additionally, we provide a statistical description of their behavior on two datasets recorded in two elderly populations and with different protocols, to hint at typical values of these variables. First, the balance of 133 elderly individuals, including 32 fallers, was measured on a relatively inexpensive, portable force platform (Wii Balance Board, Nintendo) with a 25-s open-eyes protocol. Second, the recordings of 76 elderly individuals, from an open access database commonly used to test static balance analyses, were used to compute the values of the variables on 60-s eyes-open recordings with a research laboratory standard force platform.

Sleep apnea syndrome and subthalamic stimulation in Parkinson's disease

OBJECTIVES

Τhe association between Parkinson's disease (PD) and sleep apnea syndrome (SAS) is not fully elucidated and very few studies reported on SAS outcome after deep brain stimulation (DBS). Here, we compare the clinical profile of PD patients with and without SAS and assess, for the first time, the value of pre-DBS SAS as predictor of post-DBS outcome in PD.

METHODS

Fifty patients were grouped into PD with SAS (PD-SAS+,n = 22) and without (PD-SAS-,n = 28), based on the Apnea-Hypopnea-Index (AHI≥5) in polysomnography. We used novel multivariate statistical models to compare pre-DBS profiles and assess post-DBS motor, non-motor and quality of life (QoL) changes in both groups.

RESULTS

In the entire cohort, 44% of patients had at least mild SAS (AHI≥5), while 22% had at least moderate (AHI≥15). Mean AHI was 11/h (NREM-AHI = 10.2/h and REM-AHI = 13.5/h). The two groups had equal demographics and PD characteristics, and did not differ in respect to unified Parkinson's disease rating scale (UPDRS)-II_OFF, Body-Mass-Index, polysomnographic features, RBD, depression, sleepiness and QoL scores. The PD-SAS+ group had significantly higher scores in UPDRS-III_OFF (41.1 ± 17.7 vs. 30.9 ± 11.7,p < 0.05) compared to PD-SAS- group. The groups did not differ in respect to post-DBS change in UPDRS-II, UPDRS-III, Epworth sleepiness scale, Hamilton depression rating scale and PDQ39 scores. Positive airway pressure therapy had no impact on post-DBS outcome.

CONCLUSIONS

In patients with PD and candidates for DBS, the presence of SAS is associated with increased motor signs, but not with a specific non-motor, QoL or sleep-wake profile. The presence of SAS prior to STN-DBS is not associated with worse outcome after surgery.