There is an increasing interest in the development of effective early detection and intervention strategies in severe mental illness (SMI). Ideally, these efforts should lead to the delineation of accurate staging models of SMI enabling personalized ...
OBJECTIVE: To assess both the feasibility and potential impact of predicting preventable hospital readmissions using causal machine learning applied to data from the implementation of a readmissions prevention intervention (the Transitions Program).
In this paper, we propose a Doppler spectrum-based passenger detection scheme for a CW (Continuous Wave) radar sensor in vehicle applications. First, we design two new features, referred to as an 'extended degree of scattering points' and a 'differen...
The rapid spread of COVID-19 has resulted in the shortage of medical resources, which necessitates accurate prognosis prediction to triage patients effectively. This study used the nationwide cohort of South Korea to develop a machine learning model ...
Cancer patient classification using predictive biomarkers for anti-cancer drug responses is essential for improving therapeutic outcomes. However, current machine-learning-based predictions of drug response often fail to identify robust translational...
BACKGROUND: Stunting affects up to one-third of the children in low-to-middle income countries (LMICs) and has been correlated with decline in cognitive capacity and vaccine immunogenicity. Early identification of infants at risk is critical for earl...
BACKGROUND: As one of the most common post-transcriptional modifications (PTCM) in RNA, 5-cytosine-methylation plays important roles in many biological functions such as RNA metabolism and cell fate decision. Through accurate identification of 5-meth...
BACKGROUND: Telepresence robots used to deliver a point-of-care (POC) consultation system that may provide value to enable effective decision making by healthcare providers at care sites.
RATIONALE AND OBJECTIVES: This study aimed to investigate radiologists' and radiographers' knowledge, perception, readiness, and challenges regarding Artificial Intelligence (AI) integration into radiology practice.
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