AI Medical Compendium Topic:
Infant

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Modeling asynchronous event sequences with RNNs.

Journal of biomedical informatics
Sequences of events have often been modeled with computational techniques, but typical preprocessing steps and problem settings do not explicitly address the ramifications of timestamped events. Clinical data, such as is found in electronic health re...

MR Image Super-Resolution via Wide Residual Networks With Fixed Skip Connection.

IEEE journal of biomedical and health informatics
Spatial resolution is a critical imaging parameter in magnetic resonance imaging. The image super-resolution (SR) is an effective and cost efficient alternative technique to improve the spatial resolution of MR images. Over the past several years, th...

Machine Learning for Outcome Prediction in Electroencephalograph (EEG)-Monitored Children in the Intensive Care Unit.

Journal of child neurology
The aim of this study was to evaluate the performance of models predicting in-hospital mortality in critically ill children undergoing continuous electroencephalography (cEEG) in the intensive care unit (ICU). We evaluated the performance of machine ...

Editor's notes.

Transfusion and apheresis science : official journal of the World Apheresis Association : official journal of the European Society for Haemapheresis

Using machine learning to identify patterns of lifetime health problems in decedents with autism spectrum disorder.

Autism research : official journal of the International Society for Autism Research
Very little is known about the health problems experienced by individuals with autism spectrum disorder (ASD) throughout their life course. We retrospectively analyzed diagnostic codes associated with de-identified electronic health records using a m...

Classifying dysmorphic syndromes by using artificial neural network based hierarchical decision tree.

Australasian physical & engineering sciences in medicine
Dysmorphic syndromes have different facial malformations. These malformations are significant to an early diagnosis of dysmorphic syndromes and contain distinctive information for face recognition. In this study we define the certain features of each...

Risk Assessment for Parents Who Suspect Their Child Has Autism Spectrum Disorder: Machine Learning Approach.

Journal of medical Internet research
BACKGROUND: Parents are likely to seek Web-based communities to verify their suspicions of autism spectrum disorder markers in their child. Automated tools support human decisions in many domains and could therefore potentially support concerned pare...

The Dependence of Machine Learning on Electronic Medical Record Quality.

AMIA ... Annual Symposium proceedings. AMIA Symposium
There is growing interest in applying machine learning methods to Electronic Medical Records (EMR). Across different institutions, however, EMR quality can vary widely. This work investigated the impact of this disparity on the performance of three a...

Assessing patient risk of central line-associated bacteremia via machine learning.

American journal of infection control
BACKGROUND: Central line-associated bloodstream infections (CLABSIs) contribute to increased morbidity, length of hospital stay, and cost. Despite progress in understanding the risk factors, there remains a need to accurately predict the risk of CLAB...

Too Much of a Good Thing: How Novelty Biases and Vocabulary Influence Known and Novel Referent Selection in 18-Month-Old Children and Associative Learning Models.

Cognitive science
Identifying the referent of novel words is a complex process that young children do with relative ease. When given multiple objects along with a novel word, children select the most novel item, sometimes retaining the word-referent link. Prior work i...