Latest AI and machine learning research in adhd/add for healthcare professionals.
The early recognition of clinical deterioration in hospital inpatients continues to be a major challenge in healthcare. In this work, we proposed an intelligible machine learning (iML) based EWS for predicting patient deterioration events and facilitating early nurse interventions. We compare a range of supervised learning models, including gradient boosting and logistic regression on electronic h...
There is great potential for artificial Intelligence (AI) and machine learning (ML) to support decision making in emergency departments (ED), however their implementation in routine clinical practice remains limited. The objective of this study was to assess the understanding, experience and perspectives of the wider paediatric ED workforce (nurses, doctors and support staff) in the United Kingdom...
The last three years have seen an explosion in published manuscripts analysing open-access health datasets, in many cases presenting misleading or bio...
This study aims to enhance our understanding of ADHD individuals through accelerometer analysis while developing a framework for managing data uncerta...
Substance use disorders (SUD) are a leading cause of psychiatric hospitalization among adolescents, yet the underlying diagnostic profiles and comorbi...
Family history is one the most powerful risk factor for attention-deficit/hyperactivity disorder (ADHD), yet no study has tested whether multimodal Ma...
Neurological development between the ages of 3 to 11 is crucial to the shaping of infrastructural capabilities like the executive functions that enabl...
Children with attention-deficit/hyperactivity disorder (ADHD) often face barriers to participating in organized sports, particularly when physical edu...
Accurate early prediction of neurological outcomes in comatose patients after cardiac arrest is critical for guiding therapeutic decisions and improvi...
To evaluate whether the well-established age-related reduction in antral follicle counts (AFC) is greater among women with higher concentrations of en...
Schizophrenia (SCZ) is associated with widespread gray matter volume (GMV) reductions, yet the underlying mechanisms driving these alterations remain ...
The Consumer Price Index (CPI) is a key economic indicator used by policymakers worldwide to monitor inflation and guide monetary policy decisions. In...
Parkinson's disease (PD) is an increasingly prevalent neurologic condition for which symptomatic, but not preventative, treatment is available. Drug r...
Many existing methods that use functional magnetic resonance imaging (fMRI) classify brain disorders, such as autism spectrum disorder (ASD) and att...
Sepsis is an organ dysfunction caused by a deregulated immune response to an infection. Early sepsis prediction and identification allow for timely ...
Despite advances in AI's performance and interpretability, AI advisors can undermine experts' decisions and increase the time and effort experts mus...
How can we effectively and efficiently learn node representations in signed bipartite graphs? A signed bipartite graph is a graph consisting of two ...
Latent Diffusion Models (LDMs) produce high-quality, photo-realistic images, however, the latency incurred by multiple costly inference iterations c...
The personalization model has gained significant attention in image generation yet remains underexplored for large vision-language models (LVLMs). B...
One of the most urgent problems is the overcrowding in emergency departments (EDs), caused by an aging population and rising healthcare costs. Patie...