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Extracting reliable health condition and symptom information to support machine learning

  • Hong Qing Yu
Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Abstract

Machine Learning (ML) technologies in recent times are widely applied in various areas to assist knowledge gaining and decision-making tasks and healthcare is one of the important area among these tasks. In this paper, we propose a process to identify reliable health data from online resources and process the data to enable being used by the ML technologies. As an example, we scrap a condition-symptom dataset with Natural Language Processing (NLP) features from one of the UK NHS website. In addition, we examine our data in depth by having symptom frequency, similarity and clustering analysis.

Publication Information

Output type

Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 1683-1687

Publication milestones

  • Published - 09/04/2020

Publication status

Published - 09/04/2020

Publisher

Institute of Electrical and Electronics Engineers Inc., United States

ISBN (Print)

9781728140346

External Publication IDs

  • handle.net: 10547/624769
  • Scopus: 85083553503

Host publication title

2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)

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