A Comparative Study of Natural Language Processing Algorithms Based on Cities Changing Diabetes Vulnerability Data

Abstract

(1) Background: Poor adherence to management behaviors in Chinese Type 2 diabetes mellitus (T2DM) patients leads to an uncontrolled prognosis of diabetes, which results in significant economic costs for China. It is imperative to quickly locate vulnerability factors in the management behavior of patients with T2DM. (2) Methods: In this study, a thematic analysis of the collected interview materials was conducted to construct the themes of T2DM management vulnerability. We explored the applicability of the pre-trained models based on the evaluation metrics in text classification. (3) Results: We constructed 12 themes of vulnerability related to the health and well-being of people with T2DM in Tianjin. We considered that Bidirectional Encoder Representation from Transformers (BERT) performed better in this Natural Language Processing (NLP) task with a shorter completion time. With the splitting ratio of 6:3:1 and batch size of 64 for BERT, the test accuracy was 97.71%, the completion time was 10 min 24 s, and the macro-F1 score was 0.9752. (4) Conclusions: Our results proved the applicability of NLP techniques in this specific Chinese-language medical environment. We filled the knowledge gap in the application of NLP technologies in diabetes management. Our study provided strong support for using NLP techniques to rapidly locate vulnerability factors in T2DM management.

Analyzing Online Media Articles on Diabetes using Natural Language Processing: A Comparative Study of Indian Ocean Region and France

“…This research contributes to the understanding of how the perception of telemedicine has evolved during the pandemic. Furthermore, in a separate study, Wang et al (2022) aimed to demonstrate the applicability of natural language processing (NLP) techniques in the Chinese language medical environment. They successfully utilized NLP techniques to rapidly identify vulnerability factors in the management of Type 2 diabetes (T2DM) [7].…”

“…Furthermore, in a separate study, Wang et al (2022) aimed to demonstrate the applicability of natural language processing (NLP) techniques in the Chinese language medical environment. They successfully utilized NLP techniques to rapidly identify vulnerability factors in the management of Type 2 diabetes (T2DM) [7]. Their research highlights the potential of NLP in uncovering insights in the Chinese language medical domain.…”