Jinsong Du
Biographic Data
| ID | 8186887 |
|---|---|
| NAME | Jinsong Du |
| GIVEN NAMES | Jinsong |
| FAMILY NAME | Du |
| SIGNATURE | DU J |
| AFFILIATIONS | Zaozhuang University |
| ORCID | 0000-0003-1361-9243 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 4 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2025 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
“Short” is not always “scientific”
Objective: This study aimed to systematically evaluate and compare the content quality and reliability of weight management-related short videos on TikTok and Bilibili, identify key factors influencing video quality, and develop an automated quality prediction tool to support the construction of a healthier digital health communication ecosystem. Methods: 200). Two independent researchers evaluated video quality and information reliability using …
A study on promoting AI learning and usage behaviors among health management students from the perspective of the “knowledge-belief-action” model
Introduction: The application of Artificial Intelligence (AI) in the field of health management is becoming increasingly widespread, driving health services toward precision, personalization, and intelligence. This study aims to explore the behavior patterns of health management college students in AI learning and device usage. In addition, the study also aims to enhance students' motivation and ability to apply AI by constructing a feedback orie…
A risk prediction system for depression in middle-aged and older adults grounded in machine learning and visualization technology
Introduction: Middle-aged and older adults are highly susceptible to depression. For this reason, early identification and intervention can substantially reduce its prevalence. This study innovatively proposed a visual risk prediction system for depressive symptoms and depression in middle-aged and older adults, rooted in machine learning and visualization technologies. Methods: Using cohort data from the China Health and Retirement Longitudinal …
Development of a visualized risk prediction system for sarcopenia in older adults using machine learning
Introduction: The older adult are at high risk of sarcopenia, making early identification and scientific intervention crucial for healthy aging. Methods: This study utilized data from the China Health and Retirement Longitudinal Study (CHARLS), including a cohort of 2,717 middle-aged and older adult participants. Ten machine learning algorithms, such as CatBoost, XGBoost, and NGBoost, were used to construct predictive models. Results: Among these…
No prominent works on this page.
A risk prediction system for depression in middle-aged and older adults grounded in machine learning and visualization technology
Introduction: Middle-aged and older adults are highly susceptible to depression. For this reason, early identification and intervention can substantially reduce its prevalence. This study innovatively proposed a visual risk prediction system for depressive symptoms and depression in middle-aged and older adults, rooted in machine learning and visualization technologies. Methods: Using cohort data from the China Health and Retirement Longitudinal …
Development of a visualized risk prediction system for sarcopenia in older adults using machine learning
Introduction: The older adult are at high risk of sarcopenia, making early identification and scientific intervention crucial for healthy aging. Methods: This study utilized data from the China Health and Retirement Longitudinal Study (CHARLS), including a cohort of 2,717 middle-aged and older adult participants. Ten machine learning algorithms, such as CatBoost, XGBoost, and NGBoost, were used to construct predictive models. Results: Among these…
“Short” is not always “scientific”
Objective: This study aimed to systematically evaluate and compare the content quality and reliability of weight management-related short videos on TikTok and Bilibili, identify key factors influencing video quality, and develop an automated quality prediction tool to support the construction of a healthier digital health communication ecosystem. Methods: 200). Two independent researchers evaluated video quality and information reliability using …
A study on promoting AI learning and usage behaviors among health management students from the perspective of the “knowledge-belief-action” model
Introduction: The application of Artificial Intelligence (AI) in the field of health management is becoming increasingly widespread, driving health services toward precision, personalization, and intelligence. This study aims to explore the behavior patterns of health management college students in AI learning and device usage. In addition, the study also aims to enhance students' motivation and ability to apply AI by constructing a feedback orie…
Artificial Intelligence (2 works) · Cohort (2 works) · Cohort study (2 works) · Computer Science (2 works) · Gerontology (2 works) · Interpretability (2 works) · Longitudinal study (2 works) · Machine learning (2 works) · Medicine (2 works) · Pathology (2 works)