Abstract
The automatic and accurate classification of Magnetic Resonance Imaging (MRI) radiology report is essential for the analysis and interpretation epilepsy and non-epilepsy. Since the majority of MRI radiology reports are unstructured, the manual information extraction is time-consuming and requires specific expertise. In this paper, a comprehensive method is proposed to classify epilepsy and non-epilepsy real brain MRI radiology text reports automatically. This method combines the Natural Language Processing technique and statistical Machine Learning methods. 122 real MRI radiology text reports (97 epilepsy, 25 non-epilepsy) are studied by our proposed method which consists of the following steps: (i) for a given text report our systems first cleans HTML/XML tags, tokenize, erase punctuation, normalize text, (ii) then it converts into MRI text reports numeric sequences by using index-based word encoding, (iii) then we applied the deep learning models that are uni-directional long short-term memory (LSTM) network, bidirectional long short-term memory (BiLSTM) network and convolutional neural network (CNN) for the classifying comparison of the data, (iv) finally, we used 70% of used for training, 15% for validation, and 15% for test observations. Unlike previous methods, this study encompasses the following objectives: (a) to extract significant text features from radiologic reports of epilepsy disease; (b) to ensure successful classifying accuracy performance to enhance epilepsy data attributes. Therefore, our study is a comprehensive comparative study with the epilepsy dataset obtained from numeric sequences by using index-based word encoding method applied for the deep learning models. The traditional method is numeric sequences by using index-based word encoding which has been made for the first time in the literature, is successful feature descriptor in the epilepsy data set. The BiLSTM network has shown a promising performance regarding the accuracy rates. We show that the larger sized medical text reports can be analyzed by our proposed method.
Highlights
The systems used in transferring the Magnetic Resonance Imaging (MRI) radiology reports to the electronic medical records systems are being updated continuously and integrated leading to potential researches and applications in the area of radiology [1,2,3]
Transferring MRI radiology reports to electronic health record systems, which are constantly updated, integrated, and shared data, has led to the potential for advancement in radiology research and practice
The main contribution of this study, to the real MRI reports of individuals suffering from epilepsy in Turkey by applying the traditional methods of numeric sequences by using index-based word encoding have been obtained data sets precedent with epilepsy
Summary
The systems used in transferring the MRI radiology reports to the electronic medical records systems are being updated continuously and integrated leading to potential researches and applications in the area of radiology [1,2,3]. Because the majority of MRI radiology reports are unstructured and free form language, extracting information manually is a time-consuming, and unmanageable task. Natural Language Processing (NLP) is widely used in the analysis of unstructured text data [2]. NLP techniques are rule-based and statistical Machine Learning (ML). The rule-based NLP techniques are widely used in the clinical tasks, such as recordings for the incidence findings in radiology reports and employed as string mapping using a set of predefined keywords by the experts. The ML-based techniques learn the lexical and clinical characteristics of pre-labeled report content to achieve classification [5]
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