A5079445781- Artificial Intelligence in Healthcare and Education
- Machine Learning in Healthcare
- Birth, Development, and Health
- Gestational Diabetes Research and Management
- Patient Dignity and Privacy
- Pregnancy and preeclampsia studies
- Ethics in Clinical Research
- Medical Coding and Health Information
- Artificial Intelligence in Healthcare
- Advanced Image and Video Retrieval Techniques
- Healthcare Systems and Public Health
- AI in cancer detection
- COVID-19 diagnosis using AI
- Biomedical Text Mining and Ontologies
- Focus Groups and Qualitative Methods
- Scientific Computing and Data Management
- 3D Surveying and Cultural Heritage
- Privacy, Security, and Data Protection
- Topic Modeling
- Advanced Vision and Imaging
- Cancer Risks and Factors
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...
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...
In this paper, we explore a critical yet under-investigated issue: how to learn robust and well-generalized 3D representation from pre-trained vision language models such as CLIP. Previous works have demonstrated that cross-modal distillation can provide rich useful knowledge for data. However, like most deep learning models, the resultant network is still vulnerable adversarial attacks especially iterative attack. work, propose Dual Denoising, novel framework representations It combines...
There is an increasing interest in developing LLMs for medical diagnosis to improve efficiency. Despite their alluring technological potential, there no unified and comprehensive evaluation criterion, leading the inability evaluate quality potential risks of LLMs, further hindering application treatment scenarios. Besides, current evaluations heavily rely on labor-intensive interactions with obtain diagnostic dialogues human dialogue. To tackle lack we first initially establish termed...
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...
Background: 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. Thereby, we aimed develop precise subdividable triage system improve experiences patient care.Methods: We collected 395,790 EMRs 500 dialogue groups. 387,876 (98%) each dataset was used design train model, 3,957 (1%) for testing validation. The evaluated by recommendation...
<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...