[1]李佳承,黄嘉诚,周澜,等.轻度认知功能障碍患者预后情况的评估预测模型[J].第三军医大学学报,2017,39(03):281-285.
 Li Jiacheng,Huang Jiacheng,Zhou Lan,et al.Predictive modeling for prognosis in mild cognitive impairment[J].J Third Mil Med Univ,2017,39(03):281-285.
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《第三军医大学学报》[ISSN:1000-5404/CN:51-1095/R]

卷:
39卷
期数:
2017年第03期
页码:
281-285
栏目:
临床医学
出版日期:
2017-02-15

文章信息/Info

Title:
Predictive modeling for prognosis in mild cognitive impairment
作者:
李佳承 黄嘉诚 周澜 罗万春
第三军医大学:学员旅5营,生物医学工程系生物医学材料学教研室,生物医学工程系数学与生物数学教研室
Author(s):
Li Jiacheng Huang Jiacheng Zhou Lan Luo Wanchun
Battalion 5, Cadet Brigade; Department of Biomedical Materials Sciences, College of Biomedical Engineering, Department of Mathematics and Biomathematics,  College of Biomedical Engineering, Third Military Medical University, Chongqing, 400038, China
关键词:
轻度认知功能障碍贝叶斯判别分析模糊C均值聚类
Keywords:
mild cognitive impairment Bayes discriminant analysis fuzzy c-means algorithm
分类号:
R311; R749.1
文献标志码:
A
摘要:

目的       通过数学方法建立轻度认知功能障碍患者24个月预后的判别评估模型。方法       通过对152例轻度认知功能障碍患者的27项临床指标的分析,分别运用贝叶斯判别分析、决策树、BP神经网络、模糊C均值聚类及K均值聚类进行评估预测,选择准确率较高的判别模型。结果       通过差异性检验,根据P<0.01共筛选出6项指标,建立5种评估预测模型,经过100次随机抽样诊断模拟,得到贝叶斯判别分析在轻度认知功能障碍患者预后评估的平均正确率达74.86%,最高正确率达到84.21%。结论       本研究建立的数学模型能提高轻度认知功能障碍患者24个月预后的评估准确率

Abstract:

Objective       To establish a predictive model for estimating 24-month prognosis in the patients of mild cognitive impairment based on mathematics. Methods        Twenty-seven clinical indicators of 152 cases patients with confirmed mild cognitive impairment from the Alzheimer’s Disease Neuroimaging Initiative (ADNI, https://ida.loni.ucla.edu/login.jsp) were collected and analyzed. Bayes discriminant analysis, Decision tree, BP neural network, fuzzy C-means clustering and K-means clustering were used to evaluate the prediction, and the model with higher accuracy was selected. Results        According to P<0.01, 6 indicators were selected to establish a diagnostic model by significance test. There were 5 predictive models of estimation established. After 100 times of random sampling for diagnosis simulation in the patients, Bayes discriminant analysis indicated that the average diagnosis accuracy was 74.86%, and the highest accuracy was 84.21%. Conclusion       Our established model is of high predictive accuracy for 24-month prognosis in mild cognitive impairment.

参考文献/References:

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更新日期/Last Update: 2017-02-07