Improving Multi-label Medical Text Classification by Feature Selection

Glinka, Kinga; Wozniak, Rafal; Zakrzewska, Danuta

doi:10.1109/wetice.2017.42

Cited by 11 publications

(4 citation statements)

References 14 publications

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“…Multilabel text classification (MLTC) is the application of MLC to the task of text classification and the assigning of a set of targeted labels to each sample [45], which has been part of the longstanding challenge in both academia and industry. In the biomedical domain, Du et al [46] proposed an end-to-end deep learning model ML-Net for biomedical text, while Glinka et al [47] focused on a mixture of feature selection methods, filter, and wrapper methods. In addition, Hughes et al [48] tried to classify medical text fragments at the sentence level based on a CNN, and Yogarajan et al [49] used a multilabel variant of medical text classification to enhance the prediction of concurrent medical codes.…”

Section: Multilabel Text Classificationmentioning

confidence: 99%

A Multilabel Text Classifier of Cancer Literature at the Publication Level: Methods Study of Medical Text Classification

Zhang,

Li,

Liu

et al. 2023

JMIR Med Inform

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Background Given the threat posed by cancer to human health, there is a rapid growth in the volume of data in the cancer field and interdisciplinary and collaborative research is becoming increasingly important for fine-grained classification. The low-resolution classifier of reported studies at the journal level fails to satisfy advanced searching demands, and a single label does not adequately characterize the literature originated from interdisciplinary research results. There is thus a need to establish a multilabel classifier with higher resolution to support literature retrieval for cancer research and reduce the burden of screening papers for clinical relevance. Objective The primary objective of this research was to address the low-resolution issue of cancer literature classification due to the ambiguity of the existing journal-level classifier in order to support gaining high-relevance evidence for clinical consideration and all-sided results for literature retrieval. Methods We trained a multilabel classifier with scalability for classifying the literature on cancer research directly at the publication level to assign proper content-derived labels based on the “Bidirectional Encoder Representation from Transformers (BERT) + X” model and obtain the best option for X. First, a corpus of 70,599 cancer publications retrieved from the Dimensions database was divided into a training and a testing set in a ratio of 7:3. Second, using the classification terminology of International Cancer Research Partnership cancer types, we compared the performance of classifiers developed using BERT and 5 classical deep learning models, such as the text recurrent neural network (TextRNN) and FastText, followed by metrics analysis. Results After comparing various combined deep learning models, we obtained a classifier based on the optimal combination “BERT + TextRNN,” with a precision of 93.09%, a recall of 87.75%, and an F1-score of 90.34%. Moreover, we quantified the distinctive characteristics in the text structure and multilabel distribution in order to generalize the model to other fields with similar characteristics. Conclusions The “BERT + TextRNN” model was trained for high-resolution classification of cancer literature at the publication level to support accurate retrieval and academic statistics. The model automatically assigns 1 or more labels to each cancer paper, as required. Quantitative comparison verified that the “BERT + TextRNN” model is the best fit for multilabel classification of cancer literature compared to other models. More data from diverse fields will be collected to testify the scalability and extensibility of the proposed model in the future.

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Section: Multilabel Text Classificationmentioning

confidence: 99%

A Multilabel Text Classifier of Cancer Literature at the Publication Level: Methods Study of Medical Text Classification

Zhang,

Li,

Liu

et al. 2023

JMIR Med Inform

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“…Researchers explored that several classification task when texts are labelled by several sentiment labels and by this way the average F-measure reaches 0.805. Glinka et al [22] states that in the information retrieval field multi-label text classification plays s significant role. It also discuss about the application of the feature extraction to develop effectiveness in the multilabel classification.…”

Section: Literature Surveymentioning

confidence: 99%

An efficient technique for hybrid classification and feature extraction using normalization

Kaur¹,

Bathla²

2018

IJET

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Text classification is technique for assigning the class or label to a particular document within predefined class labels. Predefined classes examples are sports, business, technical, education and science etc. Classification is supervised learning technique i.e. these classes are trained with certain features and then document is classified based on similarity measure with these trained document set. Text classification is used in many applications like assigning the label to the documents, separating the spam messages from the genuine one, filtering of text, natural language processing etc. Feature selection, extraction and classification are various phases for assigning label to any document. In this paper, PCA is used for feature extraction, ABC is used for feature selection and SVM is used for classification. PCA is improved by applying normalization-using size of features in our proposed approach. It reduces the redundant features to larger extent. There are very few research works, which have implemented PCA, ABC and SVM for complete classification. Evaluation parameters like accuracy, F-measure and G-mean are calculated to check classifier efficiency. The proposed system is deployed on 20-Newsgroup dataset. Experiment analysis proves that accuracy is improved using our proposed approach as compared to existing approaches.

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“…A cancer hallmark text classification using CNN was proposed by Baker et al, where the medical datasets were thoroughly investigated [17]. Other works in medical text classification, providing some interesting results, include the integration technique of attentive rule construction with neural networks [18], genetic programming with the data driven regular expressions evolution methodology [19], improving multilabel medical text classification by means of efficient feature selection analysis [20], multilabel learning from medical plain text with convolutional residual models [21], and ontology based two-stage approach with particle swarm optimization (PSO) [22]. A medical social media text classification integrating consumer health technology [23], NLP-based instrument for medical text classification [24], efficient text augmentation techniques for clinical case classification [25], and hybridizing the idea of deep learning with token selection for the sake of patient phenotyping [26] are some of the applications related to medical text classification in general health technology aspects.…”

Section: Introductionmentioning

confidence: 99%

Medical Text Classification Using Hybrid Deep Learning Models with Multihead Attention

Prabhakar

Won

2021

Computational Intelligence and Neuroscience

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To unlock information present in clinical description, automatic medical text classification is highly useful in the arena of natural language processing (NLP). For medical text classification tasks, machine learning techniques seem to be quite effective; however, it requires extensive effort from human side, so that the labeled training data can be created. For clinical and translational research, a huge quantity of detailed patient information, such as disease status, lab tests, medication history, side effects, and treatment outcomes, has been collected in an electronic format, and it serves as a valuable data source for further analysis. Therefore, a huge quantity of detailed patient information is present in the medical text, and it is quite a huge challenge to process it efficiently. In this work, a medical text classification paradigm, using two novel deep learning architectures, is proposed to mitigate the human efforts. The first approach is that a quad channel hybrid long short-term memory (QC-LSTM) deep learning model is implemented utilizing four channels, and the second approach is that a hybrid bidirectional gated recurrent unit (BiGRU) deep learning model with multihead attention is developed and implemented successfully. The proposed methodology is validated on two medical text datasets, and a comprehensive analysis is conducted. The best results in terms of classification accuracy of 96.72% is obtained with the proposed QC-LSTM deep learning model, and a classification accuracy of 95.76% is obtained with the proposed hybrid BiGRU deep learning model.

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Improving Multi-label Medical Text Classification by Feature Selection

Cited by 11 publications

References 14 publications

A Multilabel Text Classifier of Cancer Literature at the Publication Level: Methods Study of Medical Text Classification

A Multilabel Text Classifier of Cancer Literature at the Publication Level: Methods Study of Medical Text Classification

An efficient technique for hybrid classification and feature extraction using normalization

Medical Text Classification Using Hybrid Deep Learning Models with Multihead Attention

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