AIMC Topic: Humans

Clear Filters Showing 61431 to 61440 of 95995 articles

Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data.

Scientific reports
In many research areas scientists are interested in clustering objects within small datasets while making use of prior knowledge from large reference datasets. We propose a method to apply the machine learning concept of transfer learning to unsuperv...

Prediction of IDH and TERT promoter mutations in low-grade glioma from magnetic resonance images using a convolutional neural network.

Scientific reports
Identification of genotypes is crucial for treatment of glioma. Here, we developed a method to predict tumor genotypes using a pretrained convolutional neural network (CNN) from magnetic resonance (MR) images and compared the accuracy to that of a di...

Rule-based and machine learning algorithms identify patients with systemic sclerosis accurately in the electronic health record.

Arthritis research & therapy
BACKGROUND: Systemic sclerosis (SSc) is a rare disease with studies limited by small sample sizes. Electronic health records (EHRs) represent a powerful tool to study patients with rare diseases such as SSc, but validated methods are needed. We devel...

The implementation of natural language processing to extract index lesions from breast magnetic resonance imaging reports.

BMC medical informatics and decision making
BACKGROUND: There are often multiple lesions in breast magnetic resonance imaging (MRI) reports and radiologists usually focus on describing the index lesion that is most crucial to clinicians in determining the management and prognosis of patients. ...

Use of natural language processing to improve predictive models for imaging utilization in children presenting to the emergency department.

BMC medical informatics and decision making
OBJECTIVE: To examine the association between the medical imaging utilization and information related to patients' socioeconomic, demographic and clinical factors during the patients' ED visits; and to develop predictive models using these associated...

Forecasting one-day-forward wellness conditions for community-dwelling elderly with single lead short electrocardiogram signals.

BMC medical informatics and decision making
BACKGROUND: The accelerated growth of elderly population is creating a heavy burden to the healthcare system in many developed countries and regions. Electrocardiogram (ECG) analysis has been recognized as effective approach to cardiovascular disease...

DeepFHR: intelligent prediction of fetal Acidemia using fetal heart rate signals based on convolutional neural network.

BMC medical informatics and decision making
BACKGROUND: Fetal heart rate (FHR) monitoring is a screening tool used by obstetricians to evaluate the fetal state. Because of the complexity and non-linearity, a visual interpretation of FHR signals using common guidelines usually results in signif...

A modern approach to identifying and characterizing child asthma and wheeze phenotypes based on clinical data.

PloS one
'Asthma' is a complex disease that encapsulates a heterogeneous group of phenotypes and endotypes. Research to understand these phenotypes has previously been based on longitudinal wheeze patterns or hypothesis-driven observational criteria. The aim ...

Incorporating human and learned domain knowledge into training deep neural networks: A differentiable dose-volume histogram and adversarial inspired framework for generating Pareto optimal dose distributions in radiation therapy.

Medical physics
PURPOSE: We propose a novel domain-specific loss, which is a differentiable loss function based on the dose-volume histogram (DVH), and combine it with an adversarial loss for the training of deep neural networks. In this study, we trained a neural n...