Trung Kien Dang
A5043936919- Machine Learning in Healthcare
- Artificial Intelligence in Healthcare and Education
- Computational Physics and Python Applications
- Solar Radiation and Photovoltaics
- Solar and Space Plasma Dynamics
- Respiratory Support and Mechanisms
- Privacy-Preserving Technologies in Data
- Ultrasound in Clinical Applications
- Hemodynamic Monitoring and Therapy
- Urban Heat Island Mitigation
- Artificial Intelligence in Healthcare
- Urban Green Space and Health
- Airway Management and Intubation Techniques
- Intensive Care Unit Cognitive Disorders
- Infectious Diseases and Tuberculosis
- Radiation Dose and Imaging
- Biomedical Text Mining and Ontologies
- Sepsis Diagnosis and Treatment
- Noise Effects and Management
- Neonatal Respiratory Health Research
- Orthopedic Infections and Treatments
- Long-Term Effects of COVID-19
- Tuberculosis Research and Epidemiology
- Radiology practices and education
Oxford University Clinical Research Unit
2024
National University Health System
2018-2023
National University of Singapore
2018-2023
Abstract The goal of the SunPy project is to facilitate and promote use development community-led, free, open source data analysis software for solar physics based on scientific Python environment. achieves this by developing maintaining sunpy core package supporting an ecosystem affiliated packages. This paper describes first official stable release (version 1.0) package, as well organization infrastructure. concludes with a discussion future project.
In data-driven medical research, multi-center studies have long been preferred over single-center ones due to a single institute sometimes not having enough data obtain sufficient statistical power for certain hypothesis testings as well predictive and subgroup studies. The wide adoption of electronic health records (EHRs) has made multi-institutional collaboration much more feasible. However, concerns infrastructures, regulations, privacy, standardization present challenge sharing across...
Interpreting point-of-care lung ultrasound (LUS) images from intensive care unit (ICU) patients can be challenging, especially in low- and middle- income countries (LMICs) where there is limited training available. Despite recent advances the use of Artificial Intelligence (AI) to automate many imaging analysis tasks, no AI-enabled LUS solutions have been proven clinically useful ICUs, specifically LMICs. Therefore, we developed an AI solution that assists practitioners assessed its...
Abstract Background Diabetes mellitus (DM) is an important public health concern in Singapore and places a massive burden on care spending. Tackling chronic diseases such as DM requires innovative strategies to integrate patients' data from diverse sources use scientific discovery inform clinical practice that can help better manage the disease. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) was chosen framework for integrating with disparate formats. Objective...
Abstract Tuberculous meningitis (TBM) is the most lethal form of tuberculosis. Clinical features, such as coma, can predict death, but they are insufficient for accurate prognosis other outcomes, especially when impacted by co-morbidities HIV infection. Brain magnetic resonance imaging (MRI) characterises extent and severity disease may enable more prediction complications poor outcomes. We analysed clinical brain MRI data from a prospective longitudinal study 216 adults with TBM; 73 (34%)...
Muscle ultrasound has been shown to be a valid and safe imaging modality assess muscle wasting in critically ill patients the intensive care unit (ICU). This typically involves manual delineation measure rectus femoris cross-sectional area (RFCSA), which is subjective, time-consuming, laborious task that requires significant expertise. We aimed develop evaluate an AI tool performs automated recognition measurement of RFCSA support non-expert operators using ultrasound. Twenty were recruited...
Abstract Objective We aimed to investigate whether early tracheal intubation (TI) is associated with a reduced risk of mortality and increased ventilator‐free days (VFD). Methods performed retrospective cohort study children 0 18 years old in pediatric intensive care unit (PICU), between 2008 2017. Patient demographics, vital signs, laboratory findings were extracted. Using time‐dependent propensity score‐matched algorithm, each patient was matched another equally likely be intubated within...