Kejia Liu
Biographic Data
| ID | 9820402 |
|---|---|
| NAME | Kejia Liu |
| GIVEN NAMES | Kejia |
| FAMILY NAME | Liu |
| SIGNATURE | LIU K |
| AFFILIATIONS | DHC Software (China) |
| ORCID | 0000-0001-9104-3361 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2022 |
| H-INDEX | 0 |
Optimizing bowel preparation for colonoscopy
Background The quality of bowel preparation is an important factor in the success of colonoscopy. However, the quality of bowel preparation is often affected by multiple factors. The main objective of this study was to explore the specific factors that affect the quality of bowel preparation. Methods Patients were consecutively recruited from the gastroenterology department in Union Hospital, Tongji Medical College, Huazhong University of Science…
Predictive models for small-for-gestational-age births in women exposed to pesticides before pregnancy based on multiple machine learning algorithms
Background: The association between prenatal pesticide exposures and a higher incidence of small-for-gestational-age (SGA) births has been reported. No prediction model has been developed for SGA neonates in pregnant women exposed to pesticides prior to pregnancy. Methods: = 151) of the dataset were split at random. Traditional logistic regression (LR) method and six machine learning classifiers were used to develop prediction models for SGA neon…
No prominent works on this page.
Optimizing bowel preparation for colonoscopy
Background The quality of bowel preparation is an important factor in the success of colonoscopy. However, the quality of bowel preparation is often affected by multiple factors. The main objective of this study was to explore the specific factors that affect the quality of bowel preparation. Methods Patients were consecutively recruited from the gastroenterology department in Union Hospital, Tongji Medical College, Huazhong University of Science…
Predictive models for small-for-gestational-age births in women exposed to pesticides before pregnancy based on multiple machine learning algorithms
Background: The association between prenatal pesticide exposures and a higher incidence of small-for-gestational-age (SGA) births has been reported. No prediction model has been developed for SGA neonates in pregnant women exposed to pesticides prior to pregnancy. Methods: = 151) of the dataset were split at random. Traditional logistic regression (LR) method and six machine learning classifiers were used to develop prediction models for SGA neon…
Internal Medicine (2 works) · Medicine (2 works) · Algorithm (1 work) · Artificial Intelligence (1 work) · Bowel preparation (1 work) · Colonoscopy (1 work) · Colorectal cancer (1 work) · Colorectal Cancer Screening and Detection (1 work) · Computer Science (1 work) · Confidence interval (1 work)