Mining symptom and disease web data with NLP and Open Linked Data

Hong Qing Yu

Research Output:

Chapter in Book/Report/Conference proceeding

Conference contribution

Peer-review

Open access

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) bas

Publication Information

Output type

Research Output:

Chapter in Book/Report/Conference proceeding

Conference contribution

Peer-review

Original language

English

Publication milestones

Published - 21/08/2019

Publication status

Published - 21/08/2019

Publisher

Avestia Publishing, Canada

Publication IDs

handle.net: 10547/623512

Host publication title

Proceedings of the 5th World Congress on Electrical Engineering and Computer Systems and Sciences (EECSS’19)

Access to documents

10.11159/mvml19.108

MVML19_Paper_HongQingYu_108.pdf

Accepted author manuscript, 906.64 KB

License:CC BY-NC-ND

http://avestia.com/EECSS2019_Proceedings/files/paper/MVML/MVML_108.pdf

Final published version

Handle.net

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Related Event

Title

The 5th World Congress on Electrical Engineering and Computer Systems and Sciences

Description

The 5th World Congress on Electrical Engineering and Computer Systems and Sciences (21/08/2019-23/08/2019, Lisbon)

Event type

Conference

Date

21/08/2019 - 23/08/2019

Location

Lisbon