International journal of medical informatics
Sep 23, 2019
INTRODUCTION: Depressive disorder is one of the major public health problems among the elderly. An effective depression risk prediction model can provide insights on the disease progression and potentially inform timely targeted interventions. Theref...
BACKGROUND: Postoperative mortality occurs in 1-2% of patients undergoing major inpatient surgery. The currently available prediction tools using summaries of intraoperative data are limited by their inability to reflect shifting risk associated with...
The explosive growth of biomedical literature has created a rich source of knowledge, such as that on protein-protein interactions (PPIs) and drug-drug interactions (DDIs), locked in unstructured free text. Biomedical relation classification aims to ...
Clinical named entity recognition (CNER), which intends to automatically detect clinical entities in electronic health record (EHR), is a committed step for further clinical text mining. Recently, more and more deep learning models are used to Chines...
BACKGROUND: Structured reports have been shown to improve communication between radiologists and providers. However, some radiologists are concerned about resultant decreased workflow efficiency. We tested a machine learning-based algorithm designed ...
PURPOSE: Deep neural network-based image reconstruction has demonstrated promising performance in medical imaging for undersampled and low-dose scenarios. However, it requires large amount of memory and extensive time for the training. It is especial...
Neural networks : the official journal of the International Neural Network Society
Sep 20, 2019
OBJECTIVE: This paper argues that Brain-Inspired Spiking Neural Network (BI-SNN) architectures can learn and reveal deep in time-space functional and structural patterns from spatio-temporal data. These patterns can be represented as deep knowledge, ...
This work presents a two-stage deep learning system for Named Entity Recognition (NER) and Relation Extraction (RE) from medical texts. These tasks are a crucial step to many natural language understanding applications in the biomedical domain. Autom...
Neural networks : the official journal of the International Neural Network Society
Sep 19, 2019
Over the past decade, Deep Convolutional Neural Networks (DCNNs) have shown remarkable performance in most computer vision tasks. These tasks traditionally use a fixed dataset, and the model, once trained, is deployed as is. Adding new information to...
International journal of computer assisted radiology and surgery
Sep 19, 2019
PURPOSE : Despite its potential for improvements through supervision, deep learning-based registration approaches are difficult to train for large deformations in 3D scans due to excessive memory requirements. METHODS : We propose a new 2.5D convolut...
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