Mining Symptom and Disease Web Data with NLP and Open Linked Data
Conference paper
Authors | Yu, H. |
---|---|
Type | Conference paper |
Abstract | Machine Learning (ML) technologies in recent years are widely applied in various areas to assist knowledge gaining and decision-making on healthcare. However, there is no reliable dataset that contains semantic structured knowledge on symptom and disease enable to apply advanced machine learning algorithms such clustering or prediction. In this paper, we propose a framework that can extract data from web with apply Natural Language Processing (NLP) process and semantic annotation to create Open Linked Data (OLD) based knowledge graph. At the end, the knowledge graph can be used for ML algorithms and graph oriented Deep Learning |
Keywords | algorithms; clustering; Deep Learning; machine learning; ML; Natural Language Processing; NLP; OLD; Open Linked Data; prediction |
Year | 2019 |
Conference | 5th World Congress on Electrical Engineering and Computer Systems and Sciences (EECSS’19) Lisbon, Portugal – August, 2019 |
Digital Object Identifier (DOI) | https://doi.org/10.11159/mvml19.108 |
Web address (URL) of conference proceedings | https://avestia.com/EECSS2019_Proceedings/ |
Publication dates | Aug 2019 |
Publication process dates | |
Deposited | 13 Jun 2022 |
https://repository.derby.ac.uk/item/97216/mining-symptom-and-disease-web-data-with-nlp-and-open-linked-data
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