Supporting Digital Healthcare Services Using Semantic Web Technologies

Barisevičius, Gintaras; Coste, Martin; Geleta, David; Jurić, Damir; Khodadadi, Mohammad; Stoilos, Giorgos; Zaihrayeu, Ilya

doi:10.1007/978-3-030-00668-6_18

Cited by 15 publications

(14 citation statements)

References 17 publications

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“…Base on the above a triple extraction system was deployed in Babylon. For SRL parsing we used AllenNLP (He et al 2017), for dependency parsing the StanfordNLP parser, for providing IRIs and structure for scoring we used a medical KB constructed in Babylon Health (Stoilos et al 2018), (Barisevičius et al 2018) , while for IRI ranking we used Google's universal encoder (Cer et al 2018).…”

Section: Discussionmentioning

confidence: 99%

A System for Medical Information Extraction and Verification from Unstructured Text

Jurić

Stoilos

Melo

et al. 2020

AAAI

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A wealth of medical knowledge has been encoded in terminologies like SNOMED CT, NCI, FMA, and more. However, these resources are usually lacking information like relations between diseases, symptoms, and risk factors preventing their use in diagnostic or other decision making applications. In this paper we present a pipeline for extracting such information from unstructured text and enriching medical knowledge bases. Our approach uses Semantic Role Labelling and is unsupervised. We show how we dealt with several deficiencies of SRL-based extraction, like copula verbs, relations expressed through nouns, and assigning scores to extracted triples. The system have so far extracted about 120K relations and in-house doctors verified about 5k relationships. We compared the output of the system with a manually constructed network of diseases, symptoms and risk factors build by doctors in the course of a year. Our results show that our pipeline extracts good quality and precise relations and speeds up the knowledge acquisition process considerably.

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Section: Discussionmentioning

confidence: 99%

A System for Medical Information Extraction and Verification from Unstructured Text

Jurić

Stoilos

Melo

et al. 2020

AAAI

Self Cite

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“…In another side, Semantic technology [Seeliger et al 2019] uses formal semantics to assist AI techniques to understand language and processing information the way humans do. Also Semantic Web Technologies are used to support digital healthcare services [Barisevičius et al 2018]. The use of ontologies and semantic web technologies [Manika et al 2018;Mhammedi et al 2022] like RDF, OWL and SPARQL can furnish the essential semantics for a variety of medical domains and, besides, can help as tools for making creative solutions technology to current issues.…”

Section: Literature Reviewmentioning

confidence: 99%

Ontology-Based Decision Tree Model for Prediction of Cardiovascular Disease

Massari¹,

Gherabi²,

Mhammedi³

et al. 2022

INDJCSE

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Nowadays, cardiovascular diseases (CVD) are one of the most critical reasons for death. Thus, CVD prediction is a crucial challenge in the field of clinical data analysis. Researchers are using a variety of statistical and machine learning methods to assess immense amounts of complex medical data, to help doctors predict heart disease. In this paper, we proposed a new approach to predict CVD using ML techniques and Ontology to build an efficient ontology-based model able to predict accurately the presence of cardiac disease and establish an early diagnosis. the approach consists of extracting rules from the Decision Tree algorithm that differentiate the patients with or without cardiovascular disease then implementing these rules in the ontology reasoner using Semantic Web Rule Language (SWRL). The ontology model result reach high classification accuracy of 75% compared to the decision tree model. The approach can be employed in the medical field for the prediction of cardiovascular diseases.

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“…Furthermore, the recognition and discovery of hyponym-hypernym relations is a foundational part of constructing taxonomies, which has a range of practical applications in a variety of domains such as Healthcare (Barisevičius et al, 2018) or Fashion 1 .…”

Section: Might Be Adequately Summarised Bymentioning

confidence: 99%

Data Augmentation for Hypernymy Detection

Kober

Weeds

Bertolini

et al. 2021

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

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The automatic detection of hypernymy relationships represents a challenging problem in NLP. The successful application of stateof-the-art supervised approaches using distributed representations has generally been impeded by the limited availability of high quality training data. We have developed two novel data augmentation techniques which generate new training examples from existing ones. First, we combine the linguistic principles of hypernym transitivity and intersective modifier-noun composition to generate additional pairs of vectors, such as small dogdog or small dog -animal, for which a hypernymy relationship can be assumed. Second, we use generative adversarial networks (GANs) to generate pairs of vectors for which the hypernymy relation can also be assumed. We furthermore present two complementary strategies for extending an existing dataset by leveraging linguistic resources such as Word-Net. Using an evaluation across 3 different datasets for hypernymy detection and 2 different vector spaces, we demonstrate that both of the proposed automatic data augmentation and dataset extension strategies substantially improve classifier performance.

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Supporting Digital Healthcare Services Using Semantic Web Technologies

Cited by 15 publications

References 17 publications

A System for Medical Information Extraction and Verification from Unstructured Text

A System for Medical Information Extraction and Verification from Unstructured Text

Ontology-Based Decision Tree Model for Prediction of Cardiovascular Disease

Data Augmentation for Hypernymy Detection

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