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Using de-identified electronic health records to research mental health supported housing services: A feasibility study.

PloS one
BACKGROUND: Mental health supported housing services are a key component in the rehabilitation of people with severe and complex needs. They are implemented widely in the UK and other deinstitutionalised countries but there have been few empirical st...

Identifying resting-state effective connectivity abnormalities in drug-naïve major depressive disorder diagnosis via graph convolutional networks.

Human brain mapping
Major depressive disorder (MDD) is a leading cause of disability; its symptoms interfere with social, occupational, interpersonal, and academic functioning. However, the diagnosis of MDD is still made by phenomenological approach. The advent of neuro...

Differentiating novel coronavirus pneumonia from general pneumonia based on machine learning.

Biomedical engineering online
BACKGROUND: Chest CT screening as supplementary means is crucial in diagnosing novel coronavirus pneumonia (COVID-19) with high sensitivity and popularity. Machine learning was adept in discovering intricate structures from CT images and achieved exp...

Visual perception of liquids: Insights from deep neural networks.

PLoS computational biology
Visually inferring material properties is crucial for many tasks, yet poses significant computational challenges for biological vision. Liquids and gels are particularly challenging due to their extreme variability and complex behaviour. We reasoned ...

Disrupted rich-club network organization and individualized identification of patients with major depressive disorder.

Progress in neuro-psychopharmacology & biological psychiatry
BACKGROUND: Altered structural and functional brain networks have been extensively studied in major depressive disorder (MDD) patients. However, whether the differential connectivity patterns in the rich-club organization, assessed from structural br...

Using Machine Learning to Make Predictions in Patients Who Fall.

The Journal of surgical research
BACKGROUND: As the population ages, the incidence of traumatic falls has been increasing. We hypothesize that a machine learning algorithm can more accurately predict mortality after a fall compared with a standard logistic regression (LR) model base...

A deep learning-based method for improving reliability of multicenter diffusion kurtosis imaging with varied acquisition protocols.

Magnetic resonance imaging
Multicenter magnetic resonance imaging is gaining more popularity in large-sample projects. Since both varying hardware and software across different centers cause unavoidable data heterogeneity across centers, its impact on reliability in study outc...

Utilization of machine-learning models to accurately predict the risk for critical COVID-19.

Internal and emergency medicine
Among patients with Coronavirus disease (COVID-19), the ability to identify patients at risk for deterioration during their hospital stay is essential for effective patient allocation and management. To predict patient risk for critical COVID-19 base...

Robot-assisted pelvic urologic surgeries: is it feasible to perform under reduced tilt?

Journal of robotic surgery
To evaluate the feasibility and outcomes of performing robot-assisted pelvic surgery at a reduced angle of Trendelenburg position. This was a prospective case-control study of 67 patients in 2:1 ratio. Controls were operated with steep Trendelenburg ...