Latest AI and machine learning research in product alert for healthcare professionals.
BACKGROUND: Current medicine cannot confidently predict who will recover from post-stroke impairments. Researchers have sought to bridge this gap by treating the post-stroke prognostic problem as a machine learning problem, reporting prediction error metrics across samples of patients whose outcomes are known. This approach effectively shares prediction error equally among the patients, which is c...
CONTEXT: Neuropathic pain (NP), can be a debilitating consequence of spinal cord injury. Robotic-assisted gait training (RAGT) is an effective rehabilitation tool, but emerging evidence suggests it may prove an effective treatment for NP post-SCI. OBJECTIVE: This systematic review aims to synthesize the available evidence examining RAGT for pain reduction post-SCI, focusing on NP. METHODS: Six dat...
This study presents a non-invasive approach to monitoring post-harvest fruit quality by applying CO laser photoacoustic spectroscopy (COLPAS) to study...
Obstructive sleep apnea (OSA) may impact outcomes in acute coronary syndrome (ACS) patients. The Global Registry of Acute Coronary Events (GRACE) scor...
AimStroke often leads to impaired motor functions, particularly in upper extremities, making functional recovery essential for quality of life and ind...
OBJECTIVES/BACKGROUND: Post-traumatic headache (PTH) is a common symptom following mild traumatic brain injury (mTBI). Currently, there is no identifi...
Self-interpreting neural networks have attracted significant attention from the research community. Along this line, extensive works inherently share ...
Patients with post-COVID-19-related symptoms require active and timely support in self-management. Just-in-time adaptive interventions (JITAI) seem pr...
Conventional hardware neural networks (HW-NNs) have relied on unidirectional current flow of artificial synapses, necessitating a differential pair of...
Early detection of atrial fibrillation (AFib) is crucial for altering its natural progression and complication profile. Traditional demographic and li...
Traditional computed tomography (CT) methods for 3D reconstruction face resolution limitations and require time-consuming post-processing workflows. W...
Pharmacovigilance is essential for protecting patient health by monitoring and managing medication-related risks. Traditional methods like spontaneous...
Cement production exceeds 4.1 billion tonnes annually, emitting 2.4 billion tonnes of CO annually, necessitating improved process control. Traditional...
Connectomics is an evolving branch of neuroscience that determines structural and functional connectivity in the brain. The objective of this prospec...
The interpretability of quantum machine learning (QML) refers to the capability to provide clear and understandable explanations for the predictions a...
Drug-resistant epilepsy (DRE) patients typically require surgical intervention or neurostimulation. Therefore, accurate localization of the seizure on...
BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has led to considerable advances in medical education through technological integration. ...
Despite the extensive research on medication-related adverse events (MRAEs) in healthcare, the assessment of the present scenario is made more difficu...
Heart failure (HF) is a life-threatening condition that poses a significant challenge on public health, particularly among the elder populations. To d...
Background Effective communication skills are essential for quality medical practice and patient care, yet providing sufficient practice opportunities...