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  • Hou, Xuan  (2)
  • 2020-2024  (2)
  • 2021  (2)
  • Medicine  (2)
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  • 2020-2024  (2)
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  • 2021  (2)
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  • Medicine  (2)
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  • 1
    Online Resource
    Online Resource
    New Model for Estimation of the Age at Onset in Spinocerebellar Ataxia Type 3
    Ovid Technologies (Wolters Kluwer Health) ; 2021
    In:  Neurology Vol. 96, No. 23 ( 2021-06-08), p. e2885-e2895
    Details
    In: Neurology, Ovid Technologies (Wolters Kluwer Health), Vol. 96, No. 23 ( 2021-06-08), p. e2885-e2895
    Abstract: The aim of this study was to develop an appropriate parametric survival model to predict patient's age at onset (AAO) for spinocerebellar ataxia type 3/Machado-Joseph disease (SCA3/MJD) populations from mainland China. Methods We compared the efficiency and performance of 6 parametric survival analysis methods (exponential, weibull, log-gaussian, gaussian, log-logistic, and logistic) based on cytosine-adenine-guanine (CAG) repeat length at ATXN3 to predict the probability of AAO in the largest cohort of patients with SCA3/MJD. A set of evaluation criteria, including −2 log-likelihood statistic, Akaike information criterion (AIC), bayesian information criterion (BIC), Nagelkerke R-squared (Nagelkerke R^2), and Cox-Snell residual plot, were used to identify the best model. Results Among these 6 parametric survival models, the logistic model had the lowest −2 log-likelihood (6,560.12), AIC (6,566.12), and BIC (6,566.14) and the highest value of Nagelkerke R^2 (0.54), with the closest graph to the bisector Cox-Snell residual graph. Therefore, the logistic survival model was the best fit to the studied data. Using the optimal logistic survival model, we indicated the age-specific probability distribution of AAO according to the CAG repeat size and current age. Conclusions We first demonstrated that the logistic survival model provided the best fit for AAO prediction in patients with SCA3/MJD from mainland China. This optimal model can be valuable in clinical and research. However, the rigorous clinical testing and practice of other independent cohorts are needed for its clinical application. A unified model across multiethnic cohorts is worth further exploration by identifying regional differences and significant modifiers in AAO determination.
    Type of Medium: Online Resource
    ISSN: 0028-3878 , 1526-632X
    URL: Article
    DOI: 10.1212/WNL.0000000000012068
    RVK:
    XA 10000
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2021
    Location Call Number Limitation Availability
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    Online Resource
  • 2
    Online Resource
    Online Resource
    Prediction of the Age at Onset of Spinocerebellar Ataxia Type 3 with Machine Learning
    Wiley ; 2021
    In:  Movement Disorders Vol. 36, No. 1 ( 2021-01), p. 216-224
    Details
    In: Movement Disorders, Wiley, Vol. 36, No. 1 ( 2021-01), p. 216-224
    Abstract: In polyglutamine (polyQ) disease, the investigation of the prediction of a patient's age at onset (AAO) facilitates the development of disease‐modifying intervention and underpins the delay of disease onset and progression. Few polyQ disease studies have evaluated AAO predicted by machine‐learning algorithms and linear regression methods. Objective The objective of this study was to develop a machine‐learning model for AAO prediction in the largest spinocerebellar ataxia type 3/Machado–Joseph disease (SCA3/MJD) population from mainland China. Methods In this observational study, we introduced an innovative approach by systematically comparing the performance of 7 machine‐learning algorithms with linear regression to explore AAO prediction in SCA3/MJD using CAG expansions of 10 polyQ‐related genes, sex, and parental origin. Results Similar prediction performance of testing set and training set in each models were identified and few overfitting of training data was observed. Overall, the machine‐learning‐based XGBoost model exhibited the most favorable performance in AAO prediction over the traditional linear regression method and other 6 machine‐learning algorithms for the training set and testing set. The optimal XGBoost model achieved mean absolute error, root mean square error, and median absolute error of 5.56, 7.13, 4.15 years, respectively, in testing set 1, with mean absolute error (4.78 years), root mean square error (6.31 years), and median absolute error (3.59 years) in testing set 2. Conclusion Machine‐learning algorithms can be used to predict AAO in patients with SCA3/MJD. The optimal XGBoost algorithm can provide a good reference for the establishment and optimization of prediction models for SCA3/MJD or other polyQ diseases. © 2020 International Parkinson and Movement Disorder Society
    Type of Medium: Online Resource
    ISSN: 0885-3185 , 1531-8257
    URL: Issue
    URL: Article
    DOI: 10.1002/mds.v36.1
    DOI: 10.1002/mds.28311
    RVK:
    XA 10000
    Language: English
    Publisher: Wiley
    Publication Date: 2021
    detail.hit.zdb_id: 2041249-6
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