Northampton Electronic Collection of Theses and Research

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

Chong, N. K., Hosseinian Far, A., Daneshkhah, A. and Salari, N. (2021) Topic modelling in precision medicine with its applications in personalized diabetes management. Expert Systems. 0266-4720.

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Chong_etal_Expert_Systems_2021_Topic_Modelling_in_Precision_Medicine_with_its_Applications_in_Personalised_Diabetes_Management
Chong_etal_Expert_Systems_2021_Topic_Modelling_in_Precision_Medicine_with_its_Applications_in_Personalised_Diabetes_Management.pdf - Accepted Version
Information
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 personalised 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.
Uncontrolled Keywords:
/dk/atira/pure/sustainabledevelopmentgoals/good_health_and_well_being
Creators:
Chong, N. K., Hosseinian Far, A., Daneshkhah, A. and Salari, N.
Faculties, Divisions and Institutes:
Faculties > Faculty of Business & Law > Business Systems & Operations
Date:
30 June 2021
Date Type:
Acceptance
Journal or Publication Title:
Expert Systems
Language:
English
DOI:
10.1111/exsy.12774
ISSN:
0266-4720
Status:
Published / Disseminated
Refereed:
Yes
Related URLs:
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