Mining Symptom and Disease Web Data with NLP and Open Linked Data

Conference paper

Yu, H. 2019. Mining Symptom and Disease Web Data with NLP and Open Linked Data. 5th World Congress on Electrical Engineering and Computer Systems and Sciences (EECSS’19) Lisbon, Portugal – August, 2019. https://doi.org/10.11159/mvml19.108
AuthorsYu, H.
TypeConference 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
techniques.

Keywordsalgorithms; clustering; Deep Learning; machine learning; ML; Natural Language Processing; NLP; OLD; Open Linked Data; prediction
Year2019
Conference5th 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 proceedingshttps://avestia.com/EECSS2019_Proceedings/
Publication datesAug 2019
Publication process dates
Deposited13 Jun 2022
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