Post-stroke Anxiety Analysis via Machine Learning Methods

Post stroke anxiety is so fucking easy to explain. NO PROTOCOLS LEADING TO 100% RECOVERY. Solve that problem you fucking idiots, you don't need to analyze why anxiety exists, prevent it from occurring. PROTOCOLS NEEDED!

Post-stroke Anxiety Analysis via Machine Learning Methods

Jirui Wang¹, Defeng Zhao², Meiqing Lin¹, Xinyu Huang³ and Xiuli Shang^1*

• ¹Department of Neurology, The First Affiliated Hospital, China Medical University, Shenyang, China
• ²The First Clinical Department, China Medical University, Shenyang, China
• ³Software College, Northeastern University, Shenyang, China

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,(Survivors don't need you to predict anxiety, they need you to prevent it from occurring.) which contributes to early intervention for clinical treatment.

1. Introduction

Stroke is a medical condition in which poor blood flow to the brain results in cell death, associated with high morbidity, high disability, and high mortality across the world (Wolfe, 2000). Notably, approximately 2.5 million new stroke cases annually occur in China and the mortality rate has reached 11.48% (Sun et al., 2013; Chen et al., 2017). Mood problems such as depression, apathy, and distress are commonly reported with post-stroke (Hackett et al., 2014), but anxiety in stroke patients has been relatively neglected both in clinical and research settings, in spite of its ubiquity in the general population (Remes et al., 2016). Post-stroke anxiety (PSA) refers that stroke patients extremely concern about the prognosis status, e.g., recurrence, re-working abilities, the occurrence of fall accidents, and so on (Gilworth et al., 2009). Once stroke onset, anxiety becomes common throughout the acute phase, after months, and even after years (Lincoln et al., 2013). A systematic review and meta-analysis shows that the prevalence of anxiety disorders is 29.3% post-stroke during the first year, with 36.7% in 2 weeks, 24.1% in 2 weeks to 3 months, and 23.8% in 3–12 months (Rafsten et al., 2018). Specifically, Knapp et al. (2020) collect and analyze 53 studies and report 25.5% of stroke patients developed PSA within 1 month of stroke, 23.6% in 1 and 5 months, and 21.5% in 6 months to 1 year. A plethora of studies indicate that PSA significantly influences the living quality (Lincoln et al., 2013), which is associated with the delaying recovery of neurological function (Chun et al., 2018), and the interventions on anxiety disorders have a positive impact on the incidence of both coronary artery disease and stroke (Pérez-Piñar et al., 2017).

Given the significant impact of PSA on patient outcomes, great emphasis has been placed on risk reduction and early detection. However, the pathophysiology of PSA is still unknown and the relevant risk factors are controversial. A systematic review on 18 observational studies with 8,130 patients suggests that pre-stroke depression, stroke severity, early anxiety, and dementia (or cognitive) impairment following stroke are the main predictors of PSA, while the lack of methodological and statistical rigorously affects the validity of predictive models, which indicates future research should focus on testing predictive models on both internal and external samples to ultimately inform future clinical practice (Menlove et al., 2015). Accurate individual patient risk prediction would allow for evaluation and intervention even earlier in the pathologic process. Notably, it is critical to identify risk factors associated with PSA and build models to predict PSA.

With the rapid development of advanced technology, artificial intelligence has been applied extensively in a variety of professions. As an important tool in artificial intelligence field, machine learning (Alpaydin, 2020) has received increasing attention in the last decades, which is widely utilized in medical image processing, autonomous driving, computer vision, and so on. Classic machine learning models such as linear models, decision trees (Kamiński et al., 2018), Bayesian classifiers (Kohavi, 1996), Support Vector Machines (SVM) (Cortes and Vapnik, 1995), neural networks (Müller et al., 2012), Stochastic Gradient Descent (denoted by SGD Classifier) (Zhang, 2004), Multilayer Perceptron (denoted by MLP) (Rumelhart et al., 1986), and random forests (Breiman, 2001) have exhibited certain specific usage, i.e., there are no methods suitable for solving problems at any real-life scenarios. Stochastic gradient descent is an iterative method for optimizing an objective function with suitable smoothness properties, which can be regarded as a stochastic approximation of gradient descent optimization (Saad, 1998). A multilayer perceptron is a class of feedforward artificial neural networks, which consists of at least three layers of nodes: an input layer, a hidden layer, and an output layer. MLP utilizes a supervised learning technique called backpropagation for training, which can distinguish data that is not linearly separable (Hastie et al., 2009). An SVM maps training examples to points in space so as to maximize the width of the gap between the two categories. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall (Joachims, 1998). Random forest (RF), proposed by Breiman (2001), consists of a set of decision trees, each of which is a decision support tool that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility (Kamiński et al., 2018). Random Forest can be used in the prediction of incident delirium (Corradi et al., 2018), malignancy of pulmonary nodules (Mei et al., 2018), survival from large echocardiography, electronic health record datasets (Samad et al., 2019), and so on. The basic thought is to determine the input sample by random sampling, and the sample data obtained will be handed over to each decision tree for judgment, thereby all the results will be voted, and the result with the most votes will be used as the output. Hence, the random forest also has ensemble learning, which can improve the accuracy of the predictive model.

Inspired by such new methods, this study plans to develop proper PSA prediction models using machine learning methods. To the best of our knowledge, this is the first study to apply machine learning to predicting anxiety for post-stroke patients. This work can identify anxiety patients after stroke at an early stage, thus benefits guiding appropriate prevention and treatments to avoid leading to severe outcomes.

The main contributions of this paper are listed as follows:

(1) The main factors of PSA are analyzed in detail by traditional statistical methods between patients with/without PSA, and then all the factors with statistical difference are put into a multivariable logistic regression analysis to study in-depth.

(2) Different anxiety test scales (i.e., HADS-A, HAMA, and SAS) are taken into consideration to evaluate the degree of PSA.

(3) Classic machine learning methods such as decision tree and random forest are employed as predictive models to estimate PSA, and random forest outperforms the competitive approaches.

The rest of this paper is organized as follows. Section 2 introduces the material and methods for clinical data collection. Section 3 gives data analysis and experimental environment for machine learning methods. Section 4 exhibits experimental results of statistical analysis and PSA prediction comparison via different machine learning methods. Section 5 exhibits the discussion on the obtained results. Section 6 summarizes the whole paper and provides concluding remarks.