Computer methods and programs in biomedicine · 2026
A deep clustering Gaussian process algorithm for motor progression prediction of Parkinson's disease.
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Pan Changrong, Tian Yu, Zhou Tianshu, Chi Shengqiang, Wu Chengkai, Ma Lingyan, Ren Kang, Feng Tao, Li Jingsong
Аннотация
BACKGROUND AND OBJECTIVE: Parkinson's disease (PD) is a chronic progressive neurodegenerative disorder characterized by significant spatial and temporal heterogeneity in symptom presentation and progression, which poses a major challenge for accurate motor progression prediction. Developing a highly individualized model for predicting PD motor progression that can reflect the heterogeneity may lead to better management.
METHODS: To address this issue, we propose a novel Deep Clustering Gaussian process (DCGP) algorithm to predict PD motor progression using readily available clinical statistics and scales. The algorithm consists of three modules: a pretraining module to obtain valuable latent representations, a clustering module to capture heterogeneity among different progression patterns, and an adaptive module to fine-tune models for specific patient clusters.
RESULTS: Experiments conducted on 342 patients from the Parkinson's Progression Markers Initiative (PPMI) and 336 patients from the National Institute of Neurological Disorders and Stroke Parkinson's Disease Biomarkers Program (PDBP) demonstrated that our proposed DCGP significantly outperforms existing state-of-the-art predictive models (PPMI: RMSE = 4.607 ± 0.218, MAE = 3.504 ± 0.138, R² = 0.890 ± 0.012; PDBP: RMSE = 7.486 ± 0.208, MAE = 5.477 ± 0.157, R² = 0.652 ± 0.020). The ablation experiments show that our proposed algorithm improves predictive performance by capturing heterogeneity among different progression patterns. Based on the proposed DCGP, the magnitude of this disease progression heterogeneity was quantified as the difference between average levels and the variation over time and results reveal that the PDBP cohort exhibits greater heterogeneity in average disease levels, whereas the PPMI cohort shows greater heterogeneity in progression rate, trend, and smoothness.
CONCLUSIONS: This study proposes a novel DCGP algorithm to predict PD motor progression, which can enhance predictive performance by quantitatively capturing heterogeneity, thereby aiding doctors in making accurate predictions and providing tailored management plans for PD patients.
METHODS: To address this issue, we propose a novel Deep Clustering Gaussian process (DCGP) algorithm to predict PD motor progression using readily available clinical statistics and scales. The algorithm consists of three modules: a pretraining module to obtain valuable latent representations, a clustering module to capture heterogeneity among different progression patterns, and an adaptive module to fine-tune models for specific patient clusters.
RESULTS: Experiments conducted on 342 patients from the Parkinson's Progression Markers Initiative (PPMI) and 336 patients from the National Institute of Neurological Disorders and Stroke Parkinson's Disease Biomarkers Program (PDBP) demonstrated that our proposed DCGP significantly outperforms existing state-of-the-art predictive models (PPMI: RMSE = 4.607 ± 0.218, MAE = 3.504 ± 0.138, R² = 0.890 ± 0.012; PDBP: RMSE = 7.486 ± 0.208, MAE = 5.477 ± 0.157, R² = 0.652 ± 0.020). The ablation experiments show that our proposed algorithm improves predictive performance by capturing heterogeneity among different progression patterns. Based on the proposed DCGP, the magnitude of this disease progression heterogeneity was quantified as the difference between average levels and the variation over time and results reveal that the PDBP cohort exhibits greater heterogeneity in average disease levels, whereas the PPMI cohort shows greater heterogeneity in progression rate, trend, and smoothness.
CONCLUSIONS: This study proposes a novel DCGP algorithm to predict PD motor progression, which can enhance predictive performance by quantitatively capturing heterogeneity, thereby aiding doctors in making accurate predictions and providing tailored management plans for PD patients.