European journal of neurology · 2025
Machine Learning Predicts Risk of Falls in Parkison's Disease Patients in a Multicenter Observational Study.
СтатьяМногоцентровое исследованиеНаблюдательное исследованиеНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Malaguti Maria Chiara, Longo Chiara, Moroni Monica, Ragni Flavio, Bovo Stefano, Chierici Marco, Gios Lorenzo, Avanzino Laura, Marchese Roberta, Di Biasio Francesca, Pardini Matteo, Cerne Denise, Mandich Paola, Marenco Manuela, Uccelli Antonio, Giometto Bruno, Jurman Giuseppe, Osmani Venet, NeuroArtP3 Network
Аннотация
BACKGROUND: Postural instability and gait difficulties are key symptoms of Parkinson's disease (PD), elevating the risk of falls substantially. Falls afflict 35% to 90% of PD patients, representing a major challenge in managing the condition. Accurate prediction of fall risk and identification of contributing factors are essential for timely interventions.
OBJECTIVES: Our objective was to develop and validate a machine learning (ML) algorithm across multiple centers in Italy to accurately forecast fall risk and identify related factors using routinely collected clinical data.
METHODS: Patient data from two Italian centers (N = 251) were divided into a training cohort (N = 164) for ML model development and a validation cohort (N = 87). External validation was conducted on a subset of PPMI study patients (N = 65). We compared the performance of logistic regression (LR) and Support Vector Classifier (SVC) models trained on clinical data. The Shapley Additive exPlanations (SHAP) method was employed to examine the predictive power of individual variables.
RESULTS: In the training set, SVC outperformed LR slightly (AUC: LR = 0.779 ± 0.054, SVC = 0.792 ± 0.056). However, LR demonstrated better prediction accuracy in both internal (AUC: LR = 0.753, SVC = 0.733) and external validation cohorts (AUC: LR = 0.714, SVC = 0.676). SHAP analysis on the LR model revealed associations between fall risk and both motor and non-motor variables.
CONCLUSIONS: ML-based models effectively estimate fall risk across different clinical centers, enabling tailored interventions to enhance PD patients' quality of life. Challenges persist in predicting falls in US-based patients due to demographic and healthcare system differences.
OBJECTIVES: Our objective was to develop and validate a machine learning (ML) algorithm across multiple centers in Italy to accurately forecast fall risk and identify related factors using routinely collected clinical data.
METHODS: Patient data from two Italian centers (N = 251) were divided into a training cohort (N = 164) for ML model development and a validation cohort (N = 87). External validation was conducted on a subset of PPMI study patients (N = 65). We compared the performance of logistic regression (LR) and Support Vector Classifier (SVC) models trained on clinical data. The Shapley Additive exPlanations (SHAP) method was employed to examine the predictive power of individual variables.
RESULTS: In the training set, SVC outperformed LR slightly (AUC: LR = 0.779 ± 0.054, SVC = 0.792 ± 0.056). However, LR demonstrated better prediction accuracy in both internal (AUC: LR = 0.753, SVC = 0.733) and external validation cohorts (AUC: LR = 0.714, SVC = 0.676). SHAP analysis on the LR model revealed associations between fall risk and both motor and non-motor variables.
CONCLUSIONS: ML-based models effectively estimate fall risk across different clinical centers, enabling tailored interventions to enhance PD patients' quality of life. Challenges persist in predicting falls in US-based patients due to demographic and healthcare system differences.