Development of an individualized dementia risk prediction model using deep learning survival analysis incorporating genetic and environmental factors
Abstract Background Dementia is a major public health challenge in modern society. Early detection of high-risk dementia patients and timely intervention or treatment are of significant clinical importance. Neural network survival analysis represents the most advanced technology for survival analysi...
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| Format: | Article |
| Language: | English |
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BMC
2024-12-01
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| Series: | Alzheimer’s Research & Therapy |
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| Online Access: | https://doi.org/10.1186/s13195-024-01663-w |
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| author | Shiqi Yuan Qing Liu Xiaxuan Huang Shanyuan Tan Zihong Bai Juan Yu Fazhen Lei Huan Le Qingqing Ye Xiaoxue Peng Juying Yang Yitong Ling Jun Lyu |
| author_facet | Shiqi Yuan Qing Liu Xiaxuan Huang Shanyuan Tan Zihong Bai Juan Yu Fazhen Lei Huan Le Qingqing Ye Xiaoxue Peng Juying Yang Yitong Ling Jun Lyu |
| author_sort | Shiqi Yuan |
| collection | DOAJ |
| description | Abstract Background Dementia is a major public health challenge in modern society. Early detection of high-risk dementia patients and timely intervention or treatment are of significant clinical importance. Neural network survival analysis represents the most advanced technology for survival analysis to date. However, there is a lack of deep learning-based survival analysis models that integrate both genetic and clinical factors to develop and validate individualized dynamic dementia risk prediction models. Methods and results This study is based on a large prospective cohort from the UK Biobank, which includes a total of 41,484 participants with an average follow-up period of 12.6 years. Initially, 364 candidate features (predictor variables) were screened. The top 30 key features were then identified by ranking the importance of each predictor variable using the Gradient Boosting Machine (GBM) model. A multi-model comparison strategy was employed to evaluate the predictive performance of four survival analysis models: DeepSurv, DeepHit, Kaplan–Meier estimation, and the Cox proportional hazards model (CoxPH). The results showed that the average Harrell's C-index for the DeepSurv model was 0.743, for the DeepHit model it was 0.633, for the CoxPH model it was 0.749, and for the Kaplan–Meier estimator model it was 0.500. In addition, the average D-Calibration Survival Measure was 6.014, 4408.086, 32274.743, and 1.508, respectively. The Brier score (BS) was used to assess the importance of features for the DeepSurv dementia prediction model, and the relationship between features and dementia was visualized using a partial dependence plot (PDP). To facilitate further research, the team deployed the DeepSurv dementia prediction model on AliCloud servers and designated it as the UKB-DementiaPre Tool. Conclusion This study successfully developed and validated the DeepSurv dementia prediction model for individuals aged 60 years and above, integrating both genetic and clinical data. The model was then deployed on AliCloud servers to promote its clinical translation. It is anticipated that this prediction model will provide more accurate decision support for clinical treatment and will serve as a valuable tool for the primary prevention of dementia. |
| format | Article |
| id | doaj-art-d767a1cd89324de5b1ea87220286f742 |
| institution | Kabale University |
| issn | 1758-9193 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
| record_format | Article |
| series | Alzheimer’s Research & Therapy |
| spelling | doaj-art-d767a1cd89324de5b1ea87220286f7422025-01-05T12:10:37ZengBMCAlzheimer’s Research & Therapy1758-91932024-12-0116111710.1186/s13195-024-01663-wDevelopment of an individualized dementia risk prediction model using deep learning survival analysis incorporating genetic and environmental factorsShiqi Yuan0Qing Liu1Xiaxuan Huang2Shanyuan Tan3Zihong Bai4Juan Yu5Fazhen Lei6Huan Le7Qingqing Ye8Xiaoxue Peng9Juying Yang10Yitong Ling11Jun Lyu12Department of Neurology, The First Affiliated Hospital of Jinan UniversityDepartment of Neurology, The Second People’s Hospital of Guiyang (The Affiliated Jinyang Hospital of Guizhou Medical University)Department of Neurology, The First Affiliated Hospital of Jinan UniversityDepartment of Neurology, The First Affiliated Hospital of Jinan UniversityDepartment of Neurology, The First Affiliated Hospital of Jinan UniversityDepartment of Neurology, The Second People’s Hospital of Guiyang (The Affiliated Jinyang Hospital of Guizhou Medical University)Department of Neurology, The Second People’s Hospital of Guiyang (The Affiliated Jinyang Hospital of Guizhou Medical University)Department of Neurology, The Second People’s Hospital of Guiyang (The Affiliated Jinyang Hospital of Guizhou Medical University)Department of Neurology, The Second People’s Hospital of Guiyang (The Affiliated Jinyang Hospital of Guizhou Medical University)Department of Neurology, The Second People’s Hospital of Guiyang (The Affiliated Jinyang Hospital of Guizhou Medical University)Department of Neurology, The Second People’s Hospital of Guiyang (The Affiliated Jinyang Hospital of Guizhou Medical University)Department of Neurology, The First Affiliated Hospital of Jinan UniversityDepartment of Clinical Research, The First Affiliated Hospital of Jinan UniversityAbstract Background Dementia is a major public health challenge in modern society. Early detection of high-risk dementia patients and timely intervention or treatment are of significant clinical importance. Neural network survival analysis represents the most advanced technology for survival analysis to date. However, there is a lack of deep learning-based survival analysis models that integrate both genetic and clinical factors to develop and validate individualized dynamic dementia risk prediction models. Methods and results This study is based on a large prospective cohort from the UK Biobank, which includes a total of 41,484 participants with an average follow-up period of 12.6 years. Initially, 364 candidate features (predictor variables) were screened. The top 30 key features were then identified by ranking the importance of each predictor variable using the Gradient Boosting Machine (GBM) model. A multi-model comparison strategy was employed to evaluate the predictive performance of four survival analysis models: DeepSurv, DeepHit, Kaplan–Meier estimation, and the Cox proportional hazards model (CoxPH). The results showed that the average Harrell's C-index for the DeepSurv model was 0.743, for the DeepHit model it was 0.633, for the CoxPH model it was 0.749, and for the Kaplan–Meier estimator model it was 0.500. In addition, the average D-Calibration Survival Measure was 6.014, 4408.086, 32274.743, and 1.508, respectively. The Brier score (BS) was used to assess the importance of features for the DeepSurv dementia prediction model, and the relationship between features and dementia was visualized using a partial dependence plot (PDP). To facilitate further research, the team deployed the DeepSurv dementia prediction model on AliCloud servers and designated it as the UKB-DementiaPre Tool. Conclusion This study successfully developed and validated the DeepSurv dementia prediction model for individuals aged 60 years and above, integrating both genetic and clinical data. The model was then deployed on AliCloud servers to promote its clinical translation. It is anticipated that this prediction model will provide more accurate decision support for clinical treatment and will serve as a valuable tool for the primary prevention of dementia.https://doi.org/10.1186/s13195-024-01663-wDementiaDeepSurvRisk prediction modelSurvival analysis |
| spellingShingle | Shiqi Yuan Qing Liu Xiaxuan Huang Shanyuan Tan Zihong Bai Juan Yu Fazhen Lei Huan Le Qingqing Ye Xiaoxue Peng Juying Yang Yitong Ling Jun Lyu Development of an individualized dementia risk prediction model using deep learning survival analysis incorporating genetic and environmental factors Alzheimer’s Research & Therapy Dementia DeepSurv Risk prediction model Survival analysis |
| title | Development of an individualized dementia risk prediction model using deep learning survival analysis incorporating genetic and environmental factors |
| title_full | Development of an individualized dementia risk prediction model using deep learning survival analysis incorporating genetic and environmental factors |
| title_fullStr | Development of an individualized dementia risk prediction model using deep learning survival analysis incorporating genetic and environmental factors |
| title_full_unstemmed | Development of an individualized dementia risk prediction model using deep learning survival analysis incorporating genetic and environmental factors |
| title_short | Development of an individualized dementia risk prediction model using deep learning survival analysis incorporating genetic and environmental factors |
| title_sort | development of an individualized dementia risk prediction model using deep learning survival analysis incorporating genetic and environmental factors |
| topic | Dementia DeepSurv Risk prediction model Survival analysis |
| url | https://doi.org/10.1186/s13195-024-01663-w |
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