A predictive model of cognitive impairment in Parkinson's disease based on multivariate logistic regression

BA Mengru; YIN Xiaohong; LI Shaoyuan

doi:10.11959/j.issn.2096-6652.202413

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A predictive model of cognitive impairment in Parkinson's disease based on multivariate logistic regression

Papers and Reports | 更新时间：2024-08-06

- A predictive model of cognitive impairment in Parkinson's disease based on multivariate logistic regression
- Chinese Journal of Intelligent Science and Technology Vol. 6, Issue 2, Pages: 232-243(2024)
- 作者机构：
  
  青岛科技大学自动化与电子工程学院，山东青岛 266061
- 作者简介：
- 基金信息：
  
  Natural Science Foundation of Shandong Province(ZR2023ZD49;ZR2023MF028);Shandong Province Key Research and Development Project (Major Science and Technology Innovation Project)(2020CXGC011402);Qingdao Clinical Key Specialty Project Fund, Qingdao Science and Technology Demonstration and Guidance Project;Shandong University Qingdao Qilu Hospital Flexible Talent Project(20-3-4-37-NSH)
- DOI：10.11959/j.issn.2096-6652.202413
  CLC： TP181
- Received：23 November 2023，
  
  Revised：2024-04-29，
  
  Published：15 June 2024
- 稿件说明：
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巴梦茹,尹晓红,李少远.基于多变量逻辑回归的帕金森病认知障碍预测模型[J].智能科学与技术学报,2024,06(02):232-243.

BA Mengru,YIN Xiaohong,LI Shaoyuan.A predictive model of cognitive impairment in Parkinson's disease based on multivariate logistic regression[J].Chinese Journal of Intelligent Science and Technology,2024,06(02):232-243.
巴梦茹,尹晓红,李少远.基于多变量逻辑回归的帕金森病认知障碍预测模型[J].智能科学与技术学报,2024,06(02):232-243. DOI： 10.11959/j.issn.2096-6652.202413.

BA Mengru,YIN Xiaohong,LI Shaoyuan.A predictive model of cognitive impairment in Parkinson's disease based on multivariate logistic regression[J].Chinese Journal of Intelligent Science and Technology,2024,06(02):232-243. DOI： 10.11959/j.issn.2096-6652.202413.

摘要

帕金森病（Parkinson’s disease，PD）患者常伴随认知障碍，严重影响生活质量，因此对帕金森病认知障碍进行超前预测对于临床诊断和干预至关重要。然而，帕金森病受多变量因素（如年龄、性别、病程时间等）的耦合影响，使得认知障碍的超前预测面临严峻挑战。针对帕金森病认知障碍的多变量耦合特性，采用多变量逻辑回归方法，构建了一种新型列线图模型，旨在超前预测帕金森病患者发生认知障碍（cognitive impairment，CI）的风险。首先，应用最小绝对收缩选择算子（least absolute shrinkage and selection operator，LASSO）算法对可能影响患者认知能力的风险因素进行了分析，筛选出相关性高的临床变量。其次，采用多变量逻辑回归方法分析各变量之间的相关性，构建可视化的新型列线图模型，实现对帕金森病认知障碍的风险超前预测。最后，模型性能评估结果表明新型认知障碍预测模型具有良好的准确性、一致性和临床实用性，可显著地提高临床医生的诊断效率。此外，该模型还实现了对同一预测因子不同值的患者数量及分布的可视化对比和分析，能够辅助临床医生根据每位患者的个人风险制定个性化的医疗管理和咨询方案，有助于更早地展开对患者的干预和治疗，具备一定的临床诊断价值。

Abstract

Parkinson's disease (PD) patients were often accompanied by cognitive impairment

which seriously affected the quality of life

so the over-prediction of cognitive impairment in Parkinson's disease was crucial for clinical diagnosis and intervention. However

Parkinson's disease was affected by the coupling of multivariate factors

such as age

gender

and disease duration

which made the overprediction of cognitive impairment a serious challenge. Aiming at the multivariate coupling characteristics of cognitive impairment in Parkinson's disease

in this study

multivariate logistic regression was used to construct a novel column-linear graphical model aiming at over-predicting the risk of cognitive impairment (CI) in Parkinson's disease patients. First

the least absolute shrinkage and selection operator (LASSO) algorithm was applied to analyze the risk factors that may affect the cognitive ability of patients

and the clinical variables with high correlation were screened out. Second

multivariate logistic regression was used to analyze the correlation between variables and construct a visualized novel column-line diagram model to achieve the risk over prediction of cognitive impairment in Parkinson's disease. Finally

the results of model performance evaluation show that the novel cognitive impairment prediction model proposed in this paper has good accuracy

consistency and clinical practicability

which can significantly improve the diagnostic efficiency of clinicians; in addition

the model also realizes the visual comparison and analysis of the number and distribution of patients with different values of the same predictor

which can assist clinicians in formulating personalized healthcare management and consulting programs according to the individual risk of each patient

and help to start the intervention and treatment of the patients at an early stage

and it has a certain clinical diagnostic value.

关键词

Keywords

references

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