Latest AI and machine learning research in staffing & scheduling for healthcare professionals.
At present, the prediction of disease causal genes is mainly based on heterogeneous. Research shows that heterogeneous network contains more information and have better prediction results. In this paper, we constructed a heterogeneous network including four node types of disease, gene, phenotype and gene ontology. On this basis, we use a machine learning algorithm to predict disease-causing genes....
Pain is an integrative phenomenon coupled with dynamic interactions between sensory and contextual processes in the brain, often associated with detectable neurophysiological changes. Recent advances in brain activity recording tools and machine learning technologies have intrigued research and development of neurocomputing techniques for objective and neurophysiology-based pain detection. This pa...
Robot-assisted simple prostatectomy (RASP) and endoscopic enucleation of the prostate (EEP) are two minimally invasive alternatives to simple prostat...
The application of deep learning to generative molecule design has shown early promise for accelerating lead series development. However, questions re...
A variety of ethical concerns about artificial intelligence (AI) implementation in healthcare have emerged as AI becomes increasingly applicable and t...
Large-scale labeled training datasets have enabled deep neural networks to excel across a wide range of benchmark vision tasks. However, in many appli...
Video surveillance systems process high volumes of image data. To enable long-term retention of recorded images and because of the data transfer limit...
BACKGROUND: Difficulties in establishing diagnosis of small bowel (SB) disorders, prevented their effective treatment. This problem was largely resolv...
BACKGROUND: Robot-assisted rehabilitation for patients with stroke is promising. However, it is unclear whether additional balance training using a ba...
The purpose is to strengthen Human Resources Management (HRM) through information management using Artificial Intelligence (AI) technology. First, the...
Convolutional neural networks have achieved state-of-the-art performance for white matter (WM) tract segmentation based on diffusion magnetic resonanc...
The cloud network is rapidly growing due to a massive increase in interconnected devices and the emergence of different technologies such as the Inter...
In metabolomics, retention prediction methods have been developed based on the structural and physicochemical characteristics of analytes. Such method...
Children and adolescents could benefit from the use of predictive tools that facilitate personalized diagnoses, prognoses, and treatment selection. Su...
The objective of this study was to explore the application value of digital subtraction angiography (DSA) images optimized by deep learning algorithms...
OBJECTIVES: To propose and evaluate a convolutional neural network (CNN) algorithm for automatic detection and segmentation of mucosal thickening (MT)...
This article presents non-invasive sensing-based diagnoses of pneumonia disease, exploiting a deep learning model to make the technique non-invasive c...
Retention time prediction in high-performance liquid chromatography (HPLC) is the subject of many studies since it can improve the identification of u...
Non-targeted screening with LC/ESI/HRMS aims to identify the structure of the detected compounds using their retention time, exact mass, and fragmenta...
Medical imaging datasets usually exhibit domain shift due to the variations of scanner vendors, imaging protocols, etc. This raises the concern about ...