Clinical pharmacology and therapeutics
Apr 11, 2020
In a general inpatient population, we predicted patient-specific medication orders based on structured information in the electronic health record (EHR). Data on over three million medication orders from an academic medical center were used to train ...
Segmentation of normal organs is a critical and time-consuming process in radiotherapy. Auto-segmentation of abdominal organs has been made possible by the advent of the convolutional neural network. We utilized the U-Net, a 3D-patch-based convolutio...
The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. Deep Neural Networks (DNNs) are models composed of stacked transformations that learn tasks by examples. This technology has re...
PURPOSE: To quantitatively measure hyperreflective foci (HRF) during the progression of geographic atrophy (GA) secondary to age-related macular degeneration (AMD) using deep learning (DL) and investigate the association with local and global growth ...
Geriatrics & gerontology international
Apr 8, 2020
AIM: This study aimed to use a convolutional neural network (CNN) to investigate the associations between the time of falling and multiple complicating factors, including age, dementia severity, lower extremity strength and physical function, among n...
Predicting immunogenicity for biotherapies using patient and drug-related factors represents nowadays a challenging issue. With the growing ability to collect massive amount of data, machine learning algorithms can provide efficient predictive tools....
State-of-the-art machine learning (ML) artificial intelligence methods are increasingly leveraged in clinical predictive modeling to provide clinical decision support systems to physicians. Modern ML approaches such as artificial neural networks (ANN...
BACKGROUND: Recent advances in kernel-based Deep Learning models have introduced a new era in medical research. Originally designed for pattern recognition and image processing, Deep Learning models are now applied to survival prognosis of cancer pat...
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Apr 2, 2020
OBJECTIVE: Significant interscorer variability is found in manual scoring of arousals in polysomnographic recordings (PSGs). We propose a fully automatic method, the Multimodal Arousal Detector (MAD), for detecting arousals.
This study aimed to screen for thyroid dysfunction using Raman spectroscopy combined with an improved support vector machine (SVM). In spectral analysis, in order to further improve the classification accuracy of the SVM algorithm model, a genetic pa...
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