Identifying and Reporting Child abuse

FOPR test: a virtual reality-based technique to assess field of perception and field of regard in hemispatial neglect.

BACKGROUND: We previously proposed a novel virtual reality-based method to assess human field of perception (FOP) and field of regard (FOR), termed the FOPR test. This study assessed the diagnostic validity of the FOPR test for hemispatial neglect (HSN).

Hierarchical Long Short-Term Concurrent Memory for Human Interaction Recognition.

In this work, we aim to address the problem of human interaction recognition in videos by exploring the long-term inter-related dynamics among multiple persons. Recently, Long Short-Term Memory (LSTM) has become a popular choice to model individual dynamic for single-person action recognition due to its ability to capture the temporal motion information in a range. However, most existing LSTM-base...

DeepLPI: a multimodal deep learning method for predicting the interactions between lncRNAs and protein isoforms.

BACKGROUND: Long non-coding RNAs (lncRNAs) regulate diverse biological processes via interactions with proteins. Since the experimental methods to ide...

Multi-Scale Self-Guided Attention for Medical Image Segmentation.

Even though convolutional neural networks (CNNs) are driving progress in medical image segmentation, standard models still have some drawbacks. First,...

Combined virtual reality and haptic robotics induce space and movement invariant sensorimotor adaptation.

Prism adaptation is a method for studying visuomotor plasticity in healthy individuals, as well as for rehabilitating patients suffering spatial negle...

Dynamic MRI reconstruction with end-to-end motion-guided network.

Temporal correlation in dynamic magnetic resonance imaging (MRI), such as cardiac MRI, is informative and important to understand motion mechanisms of...

Application of Artificial Intelligence in Dentistry.

Artificial intelligence (AI) is a technology that utilizes machines to mimic intelligent human behavior. To appreciate human-technology interaction in...

Combining structured and unstructured data for predictive models: a deep learning approach.

BACKGROUND: The broad adoption of electronic health records (EHRs) provides great opportunities to conduct health care research and solve various clin...

Active semi-supervised learning for biological data classification.

Due to datasets have continuously grown, efforts have been performed in the attempt to solve the problem related to the large amount of unlabeled data...

Graph Neural Network-Based Diagnosis Prediction.

Diagnosis prediction is an important predictive task in health care that aims to predict the patient future diagnosis based on their historical medica...

Relation-Guided Representation Learning.

Deep auto-encoders (DAEs) have achieved great success in learning data representations via the powerful representability of neural networks. But most ...

Envisioning Insight-Driven Learning Based on Thick Data Analytics With Focus on Healthcare.

Detecting and analyzing patient insights from social media enables healthcare givers to better understand what patients want and also to identify thei...

Dense gate network for biomedical image segmentation.

PURPOSE: Deep learning has recently shown its outstanding performance in biomedical image semantic segmentation. Most biomedical semantic segmentation...

Data science and machine learning in anesthesiology.

Machine learning (ML) is revolutionizing anesthesiology research. Unlike classical research methods that are largely inference-based, ML is geared mor...

Real-time tumor localization with single x-ray projection at arbitrary gantry angles using a convolutional neural network (CNN).

For tumor tracking therapy, precise knowledge of tumor position in real-time is very important. A technique using single x-ray projection based on a c...

Identification of the Species Constituents of Maggot Populations Feeding on Decomposing Remains-Facilitation of the Determination of Post Mortem Interval and Time Since Tissue Infestation through Application of Machine Learning and Direct Analysis in Real Time-Mass Spectrometry.

The utilization of entomological specimens such as larvae (maggots) for the estimation of time since oviposition (i.e., egg laying) for post mortem in...

Early detection of sepsis utilizing deep learning on electronic health record event sequences.

BACKGROUND: The timeliness of detection of a sepsis incidence in progress is a crucial factor in the outcome for the patient. Machine learning models ...

Can artificial intelligence help identify elder abuse and neglect?

A health care encounter is a potentially critical opportunity to detect elder abuse and initiate intervention. Unfortunately, health care providers cu...

Detecting substance-related problems in narrative investigation summaries of child abuse and neglect using text mining and machine learning.

BACKGROUND: State child welfare agencies collect, store, and manage vast amounts of data. However, they often do not have the right data, or the data ...

Using machine learning-based lesion behavior mapping to identify anatomical networks of cognitive dysfunction: Spatial neglect and attention.

Previous lesion behavior studies primarily used univariate lesion behavior mapping techniques to map the anatomical basis of spatial neglect after rig...