Post-stroke Anxiety Analysis via Machine Learning Methods.
Post-stroke Anxiety Analysis via Machine Learning Methods.
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通过机器学习方法进行中风后焦虑分析
DOI：
10.3389/fnagi.2021.657937
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发表时间：
2021
影响因子：
4.8
通讯作者：
Shang X
中科院分区：
文献类型：
--
作者：
Wang J;Zhao D;Lin M;Huang X;Shang X
脑卒中后焦虑（PSA）近年来引起了广泛关注，但其危险因素分析及预测的研究仍是一个开放性课题。随着研究的不断深入，机器学习被广泛应用到各种场景中，并取得了越来越多的成果，为这一领域的研究带来了新的思路。本文收集了395例急性缺血性中风患者，并采用焦虑量表（即，HAMA、焦虑自评量表（SAS），将患者分为焦虑组和非焦虑组。比较两组人口学资料和一般实验室检查结果，找出有统计学差异的危险因素。对有统计学差异的因素进行多因素Logistic回归分析，得到PSA的危险因素和保护因素。经统计学分析，PSA组与非焦虑组在性别、年龄、严重脑卒中、高血压、糖尿病、饮酒、HDL-C水平等方面有显著性差异。PSA组与非焦虑组在性别、严重脑卒中、高血压、糖尿病、饮酒、HDL-C水平等方面存在差异。对HADS-A、HAMA、SAS量表进行多因素Logistic回归分析，提示高血压、糖尿病、饮酒、NIHSS评分高、血清HDL-C水平低与PSA相关。也就是说，性别、年龄、残疾、高血压、糖尿病、HDL-C和饮酒与缺血性脑卒中急性期焦虑密切相关。高血压、糖尿病、饮酒和残疾增加PSA的风险，而高血清HDL-C水平降低PSA的风险。采用几种机器学习方法分别根据HADS-A、HAMA和SAS评分来预测PSA。实验结果表明，随机森林方法在预测PSA方面优于竞争性方法，有助于临床治疗的早期干预。
Post-stroke anxiety (PSA) has caused wide public concern in recent years, and the study on risk factors analysis and prediction is still an open issue. With the deepening of the research, machine learning has been widely applied to various scenarios and make great achievements increasingly, which brings new approaches to this field. In this paper, 395 patients with acute ischemic stroke are collected and evaluated by anxiety scales (i.e., HADS-A, HAMA, and SAS), hence the patients are divided into anxiety group and non-anxiety group. Afterward, the results of demographic data and general laboratory examination between the two groups are compared to identify the risk factors with statistical differences accordingly. Then the factors with statistical differences are incorporated into a multivariate logistic regression to obtain risk factors and protective factors of PSA. Statistical analysis shows great differences in gender, age, serious stroke, hypertension, diabetes mellitus, drinking, and HDL-C level between PSA group and non-anxiety group with HADS-A and HAMA evaluation. Meanwhile, as evaluated by SAS scale, gender, serious stroke, hypertension, diabetes mellitus, drinking, and HDL-C level differ in the PSA group and the non-anxiety group. Multivariate logistic regression analysis of HADS-A, HAMA, and SAS scales suggest that hypertension, diabetes mellitus, drinking, high NIHSS score, and low serum HDL-C level are related to PSA. In other words, gender, age, disability, hypertension, diabetes mellitus, HDL-C, and drinking are closely related to anxiety during the acute stage of ischemic stroke. Hypertension, diabetes mellitus, drinking, and disability increased the risk of PSA, and higher serum HDL-C level decreased the risk of PSA. Several machine learning methods are employed to predict PSA according to HADS-A, HAMA, and SAS scores, respectively. The experimental results indicate that random forest outperforms the competitive methods in PSA prediction, which contributes to early intervention for clinical treatment.
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