An Ontology-based Two-Stage Approach to Medical Text Classification with Feature Selection by Particle Swarm Optimisation

Abdollahi, Mahdi; Gao, Xiaoying; Ghosh, Shameek; Li, Jinyan

doi:10.1109/cec.2019.8790259

Cited by 16 publications

(16 citation statements)

References 48 publications

(45 reference statements)

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“…However, due to the vastness of the collection, and the highly complex nature of the literature, the system calls for sophisticated data models. Clinical text mining applies text classification approaches to such documents to extract patient health status [1].…”

Section: Literature Reviewmentioning

confidence: 99%

Ontology Based Text Classifier for Information Extraction from Coronavirus Literature

Sivakami

Thangaraj

2021

Trends Sci

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The world is fighting an unprecedented coronavirus pandemic, and no country was prepared for it. Understanding the nature of this disease, when there is no available cure, is vital to encourage accurate clinical diagnosis and drug discovery prospects. When the amount of literature available is vast, it is important to represent the disease domain as completely as possible. The system should capture the morphology, semantics, syntax, and pragmatics of the given literature, in order to extract useful information. Also, building a classifier for a particular domain suffers from a zero frequency issue. To solve this effectively, latent topics are extracted and semantically represented in ontology to build a text classifier for coronavirus literature. The classifier is equipped with 2 components- ‘ontology’ and ‘machine learning data model’. Ontology helps to model the morphology and the semantic and pragmatic aspects of the text data through Latent Drichlet Allocation (LDA). It also preserves the contextual information in the document space, providing holistic feature representation facilities. To solve zero frequency and to extract actionable insights, a machine learning algorithm, Multi class Support Vector Machine (M-SVM), is incorporated with the ontology. It encodes features and achieves a classifier with highly discriminated classes. Further, to preserve contextual information space, and to enable data model formulation, the ontology is generated as a knowledge graph with their respective predefined classes. The resulting dataset can be used for clinical diagnosis and further research on the disease. Experimental results have shown that the proposed classifier outperforms the existing systems, with better domain representation. HIGHLIGHTS When the amount of literature available is vast, it is important to represent the disease domain as completely as possible. The system should capture the morphology, semantics, syntax, and pragmatics of the given literature, in order to extract useful information The classifier is equipped with 2 components- ‘ontology’ and ‘machine learning data model’. Ontology helps to model the morphology and the semantic and pragmatic aspects of the text data through Latent Drichlet Allocation (LDA). It also preserves the contextual information in the document space, providing holistic feature representation facilities To preserve contextual information space, and to enable data model formulation, the ontology is generated as a knowledge graph with their respective predefined classes. The resulting dataset can be used for clinical diagnosis and further research on the disease GRAPHICAL ABSTRACT

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Section: Literature Reviewmentioning

confidence: 99%

Ontology Based Text Classifier for Information Extraction from Coronavirus Literature

Sivakami

Thangaraj

2021

Trends Sci

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“…For example, Elhadad et al [22] proposed building the feature vector for web text document classification based on the WordNet ontology. Abdollahi et al [23] utilized the UMLS domain ontology to extract the key features and classify the medical text document. 2.…”

Section: Semantic Text Classification Algorithmsmentioning

confidence: 99%

Knowledge organization of node enterprises’ technological innovation under supply chain environment

Zhang

2021

Complex Intell. Syst.

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An improved text classification method based on domain ontology is proposed in this paper to organize the mass information that records node enterprises’ innovation activities under the supply chain environment. This method can classify the documents of node enterprises under the supply chain without a training set. It achieves a precision of 80% for documents’ classification, which outperforms the baseline method. Besides, the paper constructs a domain ontology of enterprises’ technological innovation under the supply chain that effectively enhances the semantic relationship between words. Therefore, it can summarize and classify the textual information generated by node enterprises in product design, production, storage, logistics, and sales.

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“…Dollah and Aono have introduced ontology-based classification approaches for biomedical abstract text classification [9]. Authors in [10][11][12][13] utilize different ontologies such as Unified Medical Language System (UMLS), Systematized Nomenclature of Medicine (SNOMED) and Medical Subject Headings (MeSH) to increase text classification accuracy.…”

Section: Feature Extraction In Medical Document Classificationmentioning

confidence: 99%

Ontology-Guided Data Augmentation for Medical Document Classification

Abdollahi

Gao

Ghosh³

et al. 2020

Artificial Intelligence in Medicine

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Extracting meaningful features from unstructured text is one of the most challenging tasks in medical document classification. The various domain specific expressions and synonyms in the clinical discharge notes make it more challenging to analyse them. The case becomes worse for short texts such as abstract documents. These challenges can lead to poor classification accuracy. As the medical input data is often not enough in the real world, in this work a novel ontology-guided method is proposed for data augmentation to enrich input data. Then, three different deep learning methods are employed to analyse the performance of the suggested approach for classification. The experimental results show that the suggested approach achieved substantial improvement in the targeted medical documents classification.

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An Ontology-based Two-Stage Approach to Medical Text Classification with Feature Selection by Particle Swarm Optimisation

Cited by 16 publications

References 48 publications

Ontology Based Text Classifier for Information Extraction from Coronavirus Literature

Ontology Based Text Classifier for Information Extraction from Coronavirus Literature

Knowledge organization of node enterprises’ technological innovation under supply chain environment

Ontology-Guided Data Augmentation for Medical Document Classification

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