Topic modelling in precision medicine with its applications in personalized diabetes management

Chong Ni Ki, Amin Hosseinian-Far, Alireza Daneshkhah, Nader Salari

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
2 Downloads (Pure)

Abstract

Advances in Internet of Things (IoT) and analytic-based systems in the past decade have found several applications in medical informatics, and have significantly facilitated healthcare decision making. Patients' data are collected through a variety of means, including IoT sensory systems, and require efficient, and accurate processing. Topic Modelling is an unsupervised machine learning algorithm for Natural Language Processing (NLP) that identifies relationships and associations within textual data. The application of Topic Modelling has been widely used on raw text data, where meaningful clusters (topics) are generated by the model. The purpose of this paper is to explore the varying methods of Topic Modelling, mostly the Latent Dirichlet allocation (LDA) model, and its applicability on personalized diabetes management. The proposed study evaluates the possibility of applying topic modelling methods on diabetes literature and genomic data in order to achieve precision medicine.
Original languageEnglish
Article numbere12774
JournalExpert Systems
Volume39
Issue number4
Early online date18 Jul 2021
DOIs
Publication statusE-pub ahead of print - 18 Jul 2021

Bibliographical note

This is the peer reviewed version of the following article: Ni Ki, C, Hosseinian-Far, A, Daneshkhah, A & Salari, N 2021, 'Topic modelling in precision medicine with its applications in personalized diabetes management', Expert Systems, vol. 39, no. 4, e12774.https://dx.doi.org/10.1111/exsy.12774, which has been published in final form at 10.1111/exsy.12774. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.

Funder

Funding Information: This research was supported by funding from Coventry University for Research Internship Programme titled ‘Probabilistic modelling for Behaviour Analysis of the social media data’.

Keywords

  • IoT-based systems for healthcare
  • latent Dirichlet allocation
  • NLP
  • personalized diabetes management
  • precision medicine
  • Topic model
  • topic modelling

ASJC Scopus subject areas

  • Artificial Intelligence
  • Theoretical Computer Science
  • Control and Systems Engineering
  • Computational Theory and Mathematics

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