Topic modelling in precision medicine with its applications in personalized diabetes management. Issue 4 (18th July 2021)

• Record Type: Journal Article
• Authors: Ni Ki, Chong; Hosseinian‐Far, Amin; Daneshkhah, Alireza; Salari, Nader
• Other Names: Chang Victor guestEditor.; Ramachandran Muthu guestEditor.; Li Chung‐Sheng guestEditor.; Zamorano Mariano Rincón guestEditor.; Tomás Rafael Martínez guestEditor.; Vicente José Manuel Ferrández guestEditor.
• Is Part Of: Expert systems. Volume 39:Issue 4(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-07-18
• Subjects: IoT‐based systems for healthcare -- latent Dirichlet allocation -- NLP -- personalized diabetes management -- precision medicine -- topic modelling; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12774
• Languages: English
• ISSNs: 0266-4720

Abstract

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.