A5046568522- Gestational Diabetes Research and Management
- Pregnancy and preeclampsia studies
- Birth, Development, and Health
- Patient Dignity and Privacy
- Cancer Risks and Factors
- Privacy, Security, and Data Protection
- Ethics in Clinical Research
- Focus Groups and Qualitative Methods
Shanghai Artificial Intelligence Laboratory
2022-2023
Beijing Academy of Artificial Intelligence
2022-2023
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...
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...
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....
<sec> <title>BACKGROUND</title> Considering the high incidence of medical privacy disclosure, it is vital importance to study doctors’ protection behavior and its influencing factors. </sec> <title>OBJECTIVE</title> We aimed explore internal mechanism doctors' patients' in Chinese public institutions using grounded theory, order construct a theoretical model framework, develop, subsequently validate scale measure this behavior. <title>METHODS</title> Combined with PMT interview data, theory...
Background: Fetal weight can be evaluated fetal growth trends and screen for abnormal growth. Predicting the birth in late gestation effectively guide clinical decisions reduce adverse pregnancy outcomes. Antenatal predication of remains challenging. On basis big data, we aim to develop a machine learning (ML) model accurate prediction prenatal weight.Methods: From 1 January 2018 31 December 2019, retrospective cohort study involving 16655 singleton live births (> 28 weeks gestation) was...
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...