Interpretable survival prediction in Parkinson’s disease with AI-assisted web deployment

Survival prediction in Parkinson’s disease (PD) remains limited, and few models have been translated into reproducible web-based tools. We analyzed 3148 patients with PD from the Chinese Parkinson’s Disease Registry, enrolled at 19 tertiary hospitals between 2018 and 2020 and followed through December 31, 2024. Cox regression, random survival forest, survival tree, and XGBoost survival models were compared using repeated five-fold cross-validation with multiple imputation performed within training folds. The final Cox model was fitted across 20 completed datasets, pooled using Rubin’s rules, and evaluated using split-first temporal validation. The 11 predictors retained in the final model covered demographic, genetic, treatment-related, motor, and non-motor domains. Cox, random survival forest, and XGBoost showed similar discrimination, with mean C-index values of 0.716 for all three models, whereas the survival tree performed lower, with a mean C-index of 0.690. For the deployed Cox model, the mean 2-, 4-, and 6-year AUCs were 0.713, 0.726, and 0.750. In temporal validation, the C-index was 0.708, with modest underestimation of absolute mortality risk. The selected Cox model was implemented in a web platform providing individualized survival estimates, with language-model modules limited to structured data entry and general PD education.

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Publication Details

Journal
npj Parkinson s Disease
Published
2026-10-03
DOI
https://doi.org/10.1038/s41531-026-01589-0
Primary Topic
Parkinson's Disease Mechanisms and Treatments
Type
article
Field-Weighted Citation Impact
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article

Interpretable survival prediction in Parkinson’s disease with AI-assisted web deployment

Yong You, Tingwei Song, Oumei Cheng, 李小云 et al.
npj Parkinson s Disease
Parkinson's Disease Mechanisms and Treatments
article

Interpretable survival prediction in Parkinson’s disease with AI-assisted web deployment

Yong You, Tingwei Song, Oumei Cheng, 李小云, Heng Wu, Shujian Li, Lingyan Yao, 严新翔, Zheng Xue, Lifang Lei, Jifeng Guo, Beisha Tang, Yuwen Zhao, Xia Zhou, Linghui Xiang, Xuejing Wang, Fei Luo, Yiting Wu, Irene X Y Wu, Yan Xu, Zhentao Zhang, Chunyu Wang, Yuan Liu, Puqing Wang, Wei Huang, Ling Chen, Li Yin, Guohua Zhao, Qian Xu, Qing Wang, Tao Wang, Zhenhua Liu, Hongxu Pan, Qiying Sun, Mei Yuan, Yuhu Zhang
article en

Abstract

Survival prediction in Parkinson’s disease (PD) remains limited, and few models have been translated into reproducible web-based tools. We analyzed 3148 patients with PD from the Chinese Parkinson’s Disease Registry, enrolled at 19 tertiary hospitals between 2018 and 2020 and followed through December 31, 2024. Cox regression, random survival forest, survival tree, and XGBoost survival models were compared using repeated five-fold cross-validation with multiple imputation performed within training folds. The final Cox model was fitted across 20 completed datasets, pooled using Rubin’s rules, and evaluated using split-first temporal validation. The 11 predictors retained in the final model covered demographic, genetic, treatment-related, motor, and non-motor domains. Cox, random survival forest, and XGBoost showed similar discrimination, with mean C-index values of 0.716 for all three models, whereas the survival tree performed lower, with a mean C-index of 0.690. For the deployed Cox model, the mean 2-, 4-, and 6-year AUCs were 0.713, 0.726, and 0.750. In temporal validation, the C-index was 0.708, with modest underestimation of absolute mortality risk. The selected Cox model was implemented in a web platform providing individualized survival estimates, with language-model modules limited to structured data entry and general PD education.

npj Parkinson s Disease
Central South University (CN), Sun Yat-sen University (CN), Hunan Provincial Center for Disease Control and Prevention (CN), Second Affiliated Hospital of Nanchang University (CN), Zhujiang Hospital (CN), The First Affiliated Hospital, Sun Yat-sen University (CN), Wuhan Union Hospital (CN), The Affiliated Yongchuan Hospital of Chongqing Medical University (CN), Henan Provincial People's Hospital (CN), Renmin Hospital of Wuhan University (CN), First Affiliated Hospital of University of South China (CN), Guangdong Academy of Medical Sciences (CN), Second Xiangya Hospital of Central South University (CN), Guangdong Provincial People's Hospital (CN), First Affiliated Hospital of Zhengzhou University (CN), Third Xiangya Hospital (CN), Xiangya Hospital Central South University (CN), Tongji Hospital (CN), Xiang Yang No.1 People's Hospital (CN), Huazhong University of Science and Technology (CN), Southern Medical University (CN), Zhejiang University (CN), Hainan Medical University (CN), University of South China (CN)
Openalex Percentile: Top 12%
Parkinson's Disease Mechanisms and Treatments
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