Yanrui Chen
Biographic Data
| ID | 8347052 |
|---|---|
| NAME | Yanrui Chen |
| GIVEN NAMES | Yanrui |
| FAMILY NAME | Chen |
| SIGNATURE | CHEN Y |
| AFFILIATIONS | Guilin Medical University |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Enhancing diabetes risk stratification through natural language processing
Background This study investigates whether integrating Natural Language Processing (NLP) with traditional clinical data improves type 2 diabetes risk prediction by extracting latent risk factors from unstructured medical text. Methods We analyzed a public dataset of 1,879 individuals. Structured variables (BMI, HbA1c, blood pressure) were normalized, while unstructured textual entries (symptom descriptions, lifestyle notes) were processed using a…
Dual assurance for healthcare and future education development
Introduction: City in Guangdong Province as the focal area. Addressing both healthcare and educational concerns, this research seeks to provide insights that can guide policy and support for this demographic. Methods: Utilizing big data analysis and deep learning algorithms, a targeted intelligent identification classification model was developed to accurately detect and classify rural low-income individuals. Additionally, a questionnaire survey …
No prominent works on this page.
Dual assurance for healthcare and future education development
Introduction: City in Guangdong Province as the focal area. Addressing both healthcare and educational concerns, this research seeks to provide insights that can guide policy and support for this demographic. Methods: Utilizing big data analysis and deep learning algorithms, a targeted intelligent identification classification model was developed to accurately detect and classify rural low-income individuals. Additionally, a questionnaire survey …
Enhancing diabetes risk stratification through natural language processing
Background This study investigates whether integrating Natural Language Processing (NLP) with traditional clinical data improves type 2 diabetes risk prediction by extracting latent risk factors from unstructured medical text. Methods We analyzed a public dataset of 1,879 individuals. Structured variables (BMI, HbA1c, blood pressure) were normalized, while unstructured textual entries (symptom descriptions, lifestyle notes) were processed using a…
Advanced Technologies in Various Fields (1 work) · Artificial Intelligence in Healthcare (1 work) · Artificial Intelligence in Healthcare and Education (1 work) · Business (1 work) · Cardiovascular Health and Risk Factors (1 work) · Cohort (1 work) · Economic growth (1 work) · Economics (1 work) · Environmental health (1 work) · Framingham Risk Score (1 work)