AIMC Topic: Child

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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 ...

Children's Imaginaries of Human-Robot Interaction in Healthcare.

International journal of environmental research and public health
This paper analyzes children’s imaginaries of Human-Robots Interaction (HRI) in the context of social robots in healthcare, and it explores ethical and social issues when designing a social robot for a children’s hospital. Based on approa...

Editor's notes.

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

Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram.

Neural networks : the official journal of the International Neural Network Society
Seizure prediction has attracted growing attention as one of the most challenging predictive data analysis efforts to improve the life of patients with drug-resistant epilepsy and tonic seizures. Many outstanding studies have reported great results i...

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...

Lung sounds classification using convolutional neural networks.

Artificial intelligence in medicine
Lung sounds convey relevant information related to pulmonary disorders, and to evaluate patients with pulmonary conditions, the physician or the doctor uses the traditional auscultation technique. However, this technique suffers from limitations. For...

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...

An improved deep learning approach for detection of thyroid papillary cancer in ultrasound images.

Scientific reports
Unlike daily routine images, ultrasound images are usually monochrome and low-resolution. In ultrasound images, the cancer regions are usually blurred, vague margin and irregular in shape. Moreover, the features of cancer region are very similar to n...

ESMAC BEST PAPER 2017: Using machine learning to overcome challenges in GMFCS level assignment.

Gait & posture
We used the random forest classifier to predict Gross Motor Function Classification System (GMFCS) levels I-IV from patient reported abilities recorded on the Gillette Functional Assessment Questionnaire (FAQ). The classifier exhibited outstanding ac...

Predicting Changes in Pediatric Medical Complexity using Large Longitudinal Health Records.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Medically complex patients consume a disproportionate amount of care resources in hospitals but still often end up with sub-optimal clinical outcomes. Predicting dynamics of complexity in such patients can potentially help improve the quality of care...