AIMC Topic: Neural Networks, Computer

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Application of a deep convolutional neural network in the diagnosis of neonatal ocular fundus hemorrhage.

Bioscience reports
There is a disparity between the increasing application of digital retinal imaging to neonatal ocular screening and slowly growing number of pediatric ophthalmologists. Assistant tools that can automatically detect ocular disorders may be needed. In ...

Application of data mining methods to improve screening for the risk of early gastric cancer.

BMC medical informatics and decision making
BACKGROUND: Although gastric cancer is a malignancy with high morbidity and mortality in China, the survival rate of patients with early gastric cancer (EGC) is high after surgical resection. To strengthen diagnosing and screening is the key to impro...

SBLC: a hybrid model for disease named entity recognition based on semantic bidirectional LSTMs and conditional random fields.

BMC medical informatics and decision making
BACKGROUND: Disease named entity recognition (NER) is a fundamental step in information processing of medical texts. However, disease NER involves complex issues such as descriptive modifiers in actual practice. The accurate identification of disease...

An automated pipeline for analyzing medication event reports in clinical settings.

BMC medical informatics and decision making
BACKGROUND: Medication events in clinical settings are significant threats to patient safety. Analyzing and learning from the medication event reports is an important way to prevent the recurrence of these events. Currently, the analysis of medicatio...

Automatic seizure detection using three-dimensional CNN based on multi-channel EEG.

BMC medical informatics and decision making
BACKGROUND: Automated seizure detection from clinical EEG data can reduce the diagnosis time and facilitate targeting treatment for epileptic patients. However, current detection approaches mainly rely on limited features manually designed by domain ...

Deep convolutional networks do not classify based on global object shape.

PLoS computational biology
Deep convolutional networks (DCNNs) are achieving previously unseen performance in object classification, raising questions about whether DCNNs operate similarly to human vision. In biological vision, shape is arguably the most important cue for reco...

From cognitive and clinical substrates to functional profiles: Disentangling heterogeneity in schizophrenia.

Psychiatry research
The relationship between neurocognition and functioning among patients with schizophrenia is well documented. However, integrating neuropsychological, clinical and psychopathological data to better investigate functional outcome still constitutes a c...

Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy.

Nature communications
Analysis of sleep for the diagnosis of sleep disorders such as Type-1 Narcolepsy (T1N) currently requires visual inspection of polysomnography records by trained scoring technicians. Here, we used neural networks in approximately 3,000 normal and abn...

Bidirectional Recurrent Auto-Encoder for Photoplethysmogram Denoising.

IEEE journal of biomedical and health informatics
Photoplethysmography (PPG) has become ubiquitous with the development of smart watches and the mobile healthcare market. However, PPG is vulnerable to various types of noises that are ever present in uncontrolled environments, and the key to obtainin...

Optimization and modelling of enzymatic polymerization of ε-caprolactone to polycaprolactone using Candida Antartica Lipase B with response surface methodology and artificial neural network.

Enzyme and microbial technology
Recently enzymatic catalysts have replaced organic and organometallic catalysts in the synthesis of bio-resorbable polymers. Enzymatic polymerization is considered as an alternative to conventional polymerization as they are less toxic, environmental...