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Label-based Topic Modeling to Enhance Medical Triage for Medical Triage Robots

Lookup NU author(s): Dr Lei ShiORCiD

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Abstract

Medical triage robots leverage natural language processing algorithms to provide accurate medical information and triage services, ultimately alleviating the strain on healthcare specialists. However, their effectiveness often hinges on the quality of topic assignment. This study proposes the Knowledge-Constrained Labeled Latent Dirichlet Allocation (KC-LLDA) method, which incorporates domain-specific knowledge constraints with LDA. KC-LLDA was compared with other existing similar topic extraction methods, which demonstrated that the proposed method is more suitable for topic modeling in the context of medical texts. In addition, this paper sets forth a novel hybrid method that combines supervised and unsupervised learning, leveraging the synergies between KC-LLDA and the BERT model, which results in a better learning of contextual information contained in medical texts, leading to the improvement of the classification accuracy. The obtained results highlight the fact that1 utilizing topic assignment can increase the efficiency of medical triage robots, ultimately improving the healthcare services provided to patients.

Publication metadata

Author(s): Feng J, Zhang R, Chen D, Shi L, Xiao C

Publication type: Article

Publication status: Published

Journal: Studies in Informatics and Control

Year: 2023

Volume: 32

Issue: 4

Pages: 37-48

Online publication date: 26/12/2023

Acceptance date: 16/12/2023

ISSN (print): 1220-1766

ISSN (electronic): 1841-429X

Publisher: ICI Bucharest

URL: https://doi.org/10.24846/v32i4y202304

DOI: 10.24846/v32i4y202304

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