A5018284294- Pregnancy and preeclampsia studies
- Gestational Diabetes Research and Management
- Artificial Intelligence in Healthcare and Education
- Birth, Development, and Health
- Artificial Intelligence in Healthcare
- Machine Learning in Healthcare
- Patient Dignity and Privacy
- COVID-19 diagnosis using AI
- Ethics in Clinical Research
- Diagnosis and Treatment of Venous Diseases
- Focus Groups and Qualitative Methods
- COVID-19 Impact on Reproduction
- Retinopathy of Prematurity Studies
- Childhood Cancer Survivors' Quality of Life
- Venous Thromboembolism Diagnosis and Management
- Maternal and Perinatal Health Interventions
- COVID-19 Clinical Research Studies
- Global Maternal and Child Health
- AI in cancer detection
- Cancer Risks and Factors
- Sepsis Diagnosis and Treatment
- Medical Coding and Health Information
- Diabetes Management and Research
- Privacy, Security, and Data Protection
- Diabetes, Cardiovascular Risks, and Lipoproteins
Shanghai Artificial Intelligence Laboratory
2022-2024
Beijing Academy of Artificial Intelligence
2022-2024
Although new technologies have increased the efficiency and convenience of medical care, patients still struggle to identify specialized outpatient departments in Chinese tertiary hospitals due a lack knowledge.
Objectives: This study aims to address the critical challenges of data integrity, accuracy, consistency, and precision in application electronic medical record (EMR) within healthcare sector, particularly context Chinese information management. The research seeks propose a solution form metadata governance framework that is efficient suitable for clinical transformation. Methods: article begins by outlining background management reviews advancements artificial intelligence (AI) technology...
Clinical guidelines for the management of individuals with type 2 diabetes mellitus endorse systematic assessment atherosclerotic cardiovascular disease risk early interventions. In this study, we aimed to develop machine learning models predict 3-year in Chinese patients.Clinical records 4,722 admitted 94 hospitals were used. The features included demographic information, histories, laboratory tests and physical examinations. Logistic regression, support vector machine, gradient boosting...
PurposeThis investigation was conceived to engineer and appraise a pioneering clinical nomogram, crafted bridge the extant chasm in literature regarding postoperative risk stratification for deep vein thrombosis (DVT) aftermath of lower extremity orthopedic procedures. This novel tool offers sophisticated discerning algorithm prediction, heretofore unmet by existing methodologies.MethodsIn this retrospective observational study, records hospitalized patients who underwent surgery were...
Background: Small for gestational age (SGA) is a condition in which fetal birthweight below the 10th percentile age, increases risk of perinatal morbidity and mortality. Therefore, early screening each pregnant woman great interest. We aimed to develop an accurate widely applicable model SGA at 21–24 weeks singleton pregnancies. Methods: This retrospective observational study included medical records 23,783 women who gave birth infants tertiary hospital Shanghai between 1 January 2018 31...
Abstract Background Fetal macrosomia is associated with an increased risk of several maternal and newborn complications. Antenatal predication fetal remains challenging. We aimed to develop a nomogram model for the prediction using real-world clinical data improve sensitivity specificity prediction. Methods In present study, we performed retrospective, observational study based on 13,403 medical records pregnant women who delivered singleton infants at tertiary hospital in Shanghai from 1...
Background Myopia, strabismus, and ptosis are common pediatric eye diseases, which have a negative impact on children adolescents in terms of visual function, mental health, health-related quality life (HRQoL). Therefore, this study focused those diseases by analyzing their risk factors HRQoL for the comprehensive management myopia, ptosis. Methods A total 363 participants (2–18 years old) were included analysis We collected demographic characteristics, lifestyle habits care these analyzed...
To assess the changes in maternal-fetal outcomes a nonepidemic designated hospital during COVID-19 pandemic.
Considering the high incidence of medical privacy disclosure, it is vital importance to study doctors' protection behavior and its influencing factors.We aim develop a scale for patients' in Chinese public institutions, following construction theoretical model framework through grounded theory, subsequently validate measure this behavior.Combined with paradigm motivation theory (PMT) semistructured interview data, research method, followed by Delphi expert group discussion methods, initial...
<sec> <title>BACKGROUND</title> Although new technologies have increased the efficiency and convenience of medical care, patients still struggle to identify specialized outpatient departments in Chinese tertiary hospitals due a lack knowledge. </sec> <title>OBJECTIVE</title> The objective our study was develop precise subdividable triage system improve experiences patient care. <title>METHODS</title> We collected 395,790 electronic records (EMRs) 500 dialogue groups. EMRs were divided into 3...
Objective: To develop a nomogram model for prediction of macrosomia with routine clinical data in pregnant women Chinese polulation.Methods: The present study retrospectively analyzed the medical records who delivered singleton infants tertiary hospital Shanghai from January 1st,2018 to December 31st, 2019. were randomly divided into two groups 4:1 ratio generate and validate model. independent risk factors by multivariate logistic regression, predict was established verified R software....
Abstract Aim: To develop a nomogram model for the prediction of macrosomia using real-world clinical data. Methods: In present study, we retrospectively analyzed medical records pregnant women who delivered singleton infants at tertiary hospital in Shanghai from 1 January 2018 through 31 December 2019. We extracted data total 13,403 this with original dataset split into training set (n = 9,382) and validation 4,021) 7:3 ratio to generate validate our model. The independent risk factors were...