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

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

doi:10.1111/exsy.12774

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

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

Research Centre for Computational Science and Mathematical Modelling

Research output: Contribution to journal › Article › peer-review

14 Citations (Scopus)

115 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 language	English
Article number	e12774
Journal	Expert Systems
Volume	39
Issue number	4
Early online date	18 Jul 2021
DOIs	https://doi.org/10.1111/exsy.12774
Publication status	Published - May 2022

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1111/exsy.12774

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Cite this

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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.",

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

Abstract

Bibliographical note

Funder

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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