Various Approaches for Predicting Stroke Prognosis using Magnetic Resonance Imaging Text Records (2020)

Abstract: Stroke is one of the leading causes of death and disability worldwide. Stroke is treatable, but it is prone to disability after treatment and must be prevented. To grasp the degree of disability caused by stroke, we use magnetic resonance imaging text records to predict stroke and measure the performance according to the document-level and sentence-level representation. As a result of the experiment, the document-level representation shows better performance.

## introduction stroke is a neurologic disorder that is characterized by an acute disruption of cerebral blood flow and corresponding symptoms lasting more than 24 hours. stroke is one of the leading causes of death and disability worldwide @xcite . as the incidence of stroke is increasing and proportion of stroke survivors with a disability is also increasing recently, there are increasing demands of proper diagnosis and long-term treatment strategy to reduce the global burden of stroke @xcite . among the diagnostic tools for stroke, the most important imaging methods are brain computed tomography (ct) and magnetic resonance image (mri). we can obtain vascular images and various functional images by brain mri. its usefulness can change recent paradigm of the stroke treatment @xcite . stroke is treatable, but it is prone to disability after treatment and must be prevented in advance @xcite . to grasp the degree of disability caused by stroke, many studies have been conducted to predict stroke prognosis in recent years @xcite @xcite . @xcite used the medical examination results of 3,605 patients as input features. for input features, a total of 76 features are extracted as results of medical tests such as 12-lead electrocardiography, chest x-ray, lipid profiles, standard blood tests, and other diagnoses. among these features, only 19 features were used through feature selection, and these features were input to the bayesian network to predict the prognosis of stroke. @xcite used several medical examination records, such as ct and mri, from admission to discharge of 425 patients, as input features. these features are input into machine learning models such as logistic regression, decision tree, support vector machine, random forest, and extreme gradient boosting (xgb) model to predict stroke prognosis. previous studies have predicted the prognosis of stroke using various diagnostic results. since a vast number of tests are required for these prediction methods, a burden of a lot of time and cost may occur from the patient's point of view. in this study, the prognosis of stroke is predicted using only mri text records created after radiologists analyze mri images. stroke prognosis prediction using mri texts corresponds to the text classification task. recently, the text classification task is showing good performance through a convolutional neural network (cnn) which extracts local information of adjacent words, long short-term memory (lstm), which is good in the sequential data processing, bidirectional lstm (bi-lstm), which has forwardbackward lstm structure, xgb, which is levelwise tree-based learning algorithms of gradient boosting method, and light gradient boosting machine (lgbm), which is leaf-wise tree-based learning algorithms of gradient boosting method @xcite @xcite . few widely known studies have used mri text recordings to predict stroke prognosis. in this study, rather than improving the performance, the performance difference according to the document representation method is identified and analyzed. in this paper, mri text is represented at the document level or sentence level. document-level representation is an approach that represents the entire document as a vector and considers it as an input and analyzes it. and sentence-level representation is an approach that analyzes the entire document by representing each sentence constituting the document as individual vectors and inputting them. this paper is composed as follows. section 2 is about a dataset and describes the mri text records used in this study. section 3 describes the architecture of the model corresponding to each representation regarding figure 1 . section 4 shows experimental results, including the hyperparameters used in the experiment, and compares and analyzes the results of various representation levels. section 5 describes a conclusion, which outlines the summary and plans for this study. ## dataset mri text records used in this study are collected from people hospitalized with acute ischemic stroke at hallym university chuncheon sacred ## document representation in this study, two document representation methods, document-level and sentence-level, are used to predict the prognosis of stroke. ## experiments english alphabet and stopwords, and all the texts are changed to lower case letters and then experimented. for the experiment, the entire data is divided in the ratio of training set: test @xmath0: 1, and the training set is again divided into 5-fold. among the training set divided into folds, four folds learn a model, and the other fold validates a trained model. the performance of the model is measured by applying the test set to the trained model. ## conclusion in this study, the performance is compared and analyzed according to the document-level and the sentence-level representation methods using mri 6 https://github.com/microsoft/lightgbm/blob/master/docs/parameters.rst since the number of datasets used in this study is unbalanced, it may adversely affect performance improvements. therefore, in future studies, we will include data used by other hospitals in the data used by this study to make more exquisite models. also, based on the results of this study, we will investigate studies that detect a variety of diseases using neural networks based on a document-level representation. accordingly, we will apply the latest language model and various neural networks to improve performance for stroke prognosis prediction.