Mengdie Liu
Biographic Data
| ID | 7943604 |
|---|---|
| NAME | Mengdie Liu |
| GIVEN NAMES | Mengdie |
| FAMILY NAME | Liu |
| SIGNATURE | LIU M |
| AFFILIATIONS | Xuzhou Medical College |
| ORCID | 0000-0002-7392-8315 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Multimodal data-driven prognostic model for predicting long-term outcomes in older adult patients with sarcopenia
Background Sarcopenia (SP) is a progressive, age-related disease that may result in various adverse health outcomes and even mortality in older adults. Accurately predicting the mortality risk of older adults with SP is essential for informed clinical decision-making. This study aims to utilize machine learning techniques that incorporate sociodemographic factors, health-related metrics, lifestyle variables, and biomarker data to improve risk str…
Association of Sleep Patterns with Type 2 Diabetes Mellitus
Sleep duration, sleep quality and circadian rhythm disruption indicated by sleep chronotype are associated with type 2 diabetes. Sleep involves multiple dimensions that are closely interrelated. However, the sleep patterns of the population, and whether these sleep patterns are significantly associated with type 2 diabetes, are unknown when considering more sleep dimensions. Our objective was to explore the latent classes of sleep patterns in the…
No prominent works on this page.
Association of Sleep Patterns with Type 2 Diabetes Mellitus
Sleep duration, sleep quality and circadian rhythm disruption indicated by sleep chronotype are associated with type 2 diabetes. Sleep involves multiple dimensions that are closely interrelated. However, the sleep patterns of the population, and whether these sleep patterns are significantly associated with type 2 diabetes, are unknown when considering more sleep dimensions. Our objective was to explore the latent classes of sleep patterns in the…
Multimodal data-driven prognostic model for predicting long-term outcomes in older adult patients with sarcopenia
Background Sarcopenia (SP) is a progressive, age-related disease that may result in various adverse health outcomes and even mortality in older adults. Accurately predicting the mortality risk of older adults with SP is essential for informed clinical decision-making. This study aims to utilize machine learning techniques that incorporate sociodemographic factors, health-related metrics, lifestyle variables, and biomarker data to improve risk str…
Internal Medicine (2 works) · Medicine (2 works) · Body Composition Measurement Techniques (1 works) · Chronotype (1 works) · Circadian rhythm (1 works) · Computer Science (1 works) · Confounding (1 works) · Cross-sectional study (1 works) · Demography (1 works) · Demography (1 works)