Lijin Zhou
Biographic Data
| ID | 10014718 |
|---|---|
| NAME | Lijin Zhou |
| GIVEN NAMES | Lijin |
| FAMILY NAME | Zhou |
| SIGNATURE | ZHOU L |
| AFFILIATIONS | Beijing Chao-Yang Hospital |
| ORCID | 0000-0002-6960-5951 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Development and validation of risk prediction models for high-risk patients with non-traumatic acute abdominal pain
Purpose: This study develops and validates a machine learning-based model to help triage nurses identify high-risk patients with non-traumatic acute abdominal pain, enhancing accuracy and safety. Patients and methods: Utilizing information technology, a data collection form was embedded into the electronic pre-triage systems of the emergency departments in two tertiary general hospitals (Shanghai Tongren Hospital and the First Affiliated People's…
Scoliosis among children in Qinghai-Tibetan Plateau of China
Background The average altitude of Qinghai-Tibetan Plateau is 4,500 m and most of the residents are Tibetan ethnicity. The purpose of this study was to investigate the prevalence of scoliosis and associated factors among children in this region through a scoliosis screening program. Methods A cross-sectional study was preformed between May 2020 and December 2020 in Qinghai-Tibetan Plateau. A total of 9,856 children aged 6–17 years from schools an…
No prominent works on this page.
Scoliosis among children in Qinghai-Tibetan Plateau of China
Background The average altitude of Qinghai-Tibetan Plateau is 4,500 m and most of the residents are Tibetan ethnicity. The purpose of this study was to investigate the prevalence of scoliosis and associated factors among children in this region through a scoliosis screening program. Methods A cross-sectional study was preformed between May 2020 and December 2020 in Qinghai-Tibetan Plateau. A total of 9,856 children aged 6–17 years from schools an…
Development and validation of risk prediction models for high-risk patients with non-traumatic acute abdominal pain
Purpose: This study develops and validates a machine learning-based model to help triage nurses identify high-risk patients with non-traumatic acute abdominal pain, enhancing accuracy and safety. Patients and methods: Utilizing information technology, a data collection form was embedded into the electronic pre-triage systems of the emergency departments in two tertiary general hospitals (Shanghai Tongren Hospital and the First Affiliated People's…
Appendicitis Diagnosis and Management (1 work) · Artificial Intelligence in Healthcare and Education (1 work) · Biology (1 work) · Clinical prediction rule (1 work) · Cross-sectional study (1 work) · Demography (1 work) · Epidemiology (1 work) · High Altitude and Hypoxia (1 work) · Hip disorders and treatments (1 work) · Internal Medicine (1 work)