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

Expert Systems - Tập 39 Số 4 - 2022
Chong Ni Ki1, Amin Hosseinian‐Far2, Alireza Daneshkhah3, Nader Salari4
1School of Computing, Electronics and Mathematics, Coventry University, Coventry, UK
2Centre for Sustainable Business Practices, University of Northampton, Northampton, UK
3Research Centre for Computational Science and Mathematical Modelling, Coventry University, Coventry, UK
4Department of Biostatistics, Kermanshah University of Medical Sciences, Kermanshah, Iran

Tóm tắt

AbstractAdvances 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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Tập 39 Số 4

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