Technology and health care : official journal of the European Society for Engineering and Medicine
39973846
BackgroundPoorly regulated and insufficiently maintained medical devices (MDs) carry high risk on safety and performance parameters impacting the clinical effectiveness and efficiency of patient diagnosis and treatment. After the MD directive (MDD) h...
OBJECTIVES: Developing and validating interpretable machine learning (ML) models for predicting whether triaged patients need to be admitted to the intensive care unit (ICU).
OBJECTIVE: To determine whether readily available patient, ultrasound and treatment outcome data can be used to develop, validate and externally test two machine learning (ML) models for predicting the success of expectant and medical management of m...
International journal of medical informatics
40015152
BACKGROUND: The 30-day hospital readmission rate is a key indicator of healthcare quality and system efficiency. This study aimed to develop machine-learning (ML) models to predict unplanned 30-day readmissions in older patients with ischemic stroke ...
This paper investigates the integration of affective computing techniques using biophysical data to advance emotionally aware machines and enhance child-robot interaction (CRI). By leveraging interdisciplinary insights from neuroscience, psychology, ...
Stroke is a significant health concern in China. Differences in stroke risk between rural and urban areas have been highlighted in prior research. However, there is a scarcity of studies on urban-rural differences in predicting stroke. This study aim...
In this study, we developed the autonomous lab (ANL), which is a system based on robotics and artificial intelligence (AI) to conduct biotechnology experiments and formulate scientific hypotheses. This system was designed with modular devices and Bay...
Accurate identification of plastic materials from post-consumer food container and packaging waste is crucial for enhancing the purity and added value of recycled materials, thereby promoting recycling and addressing the issue of plastic pollution. H...
IEEE transactions on bio-medical engineering
40030245
This study introduces an innovative approach combining deep-learning techniques with classical physics-based electrocardiographic imaging (ECGI) methods. Our objective is to enhance the accuracy and robustness of ECGI reconstructions. We reshape the ...
OBJECTIVES: Develop an interpretable machine learning model to detect patients with newly diagnosed psoriatic arthritis (PsA) in a cohort of psoriasis patients and identify important clinical indicators of PsA in primary care.