AIMC Topic: Neural Networks, Computer

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Boosting ICD multi-label classification of health records with contextual embeddings and label-granularity.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: This work deals with clinical text mining, a field of Natural Language Processing applied to biomedical informatics. The aim is to classify Electronic Health Records with respect to the International Classification of Diseas...

An enhanced deep learning approach for brain cancer MRI images classification using residual networks.

Artificial intelligence in medicine
Cancer is the second leading cause of death after cardiovascular diseases. Out of all types of cancer, brain cancer has the lowest survival rate. Brain tumors can have different types depending on their shape, texture, and location. Proper diagnosis ...

Body Composition Analysis of Computed Tomography Scans in Clinical Populations: The Role of Deep Learning.

Lifestyle genomics
BACKGROUND: Body composition is increasingly being recognized as an important prognostic factor for health outcomes across cancer, liver cirrhosis, and critically ill patients. Computed tomography (CT) scans, when taken as part of routine care, provi...

Using machine learning models to improve stroke risk level classification methods of China national stroke screening.

BMC medical informatics and decision making
BACKGROUND: With the character of high incidence, high prevalence and high mortality, stroke has brought a heavy burden to families and society in China. In 2009, the Ministry of Health of China launched the China national stroke screening and interv...

Cuneate spiking neural network learning to classify naturalistic texture stimuli under varying sensing conditions.

Neural networks : the official journal of the International Neural Network Society
We implemented a functional neuronal network that was able to learn and discriminate haptic features from biomimetic tactile sensor inputs using a two-layer spiking neuron model and homeostatic synaptic learning mechanism. The first order neuron mode...

Computational modeling of the monoaminergic neurotransmitter and male neuroendocrine systems in an analysis of therapeutic neuroadaptation to chronic antidepressant.

European neuropsychopharmacology : the journal of the European College of Neuropsychopharmacology
Second-line depression treatment involves augmentation with one (rarely two) additional drugs, of chronic administration of a selective serotonin reuptake inhibitor (SSRI), which is the first-line depression treatment. Unfortunately, many depressed p...

A multi-path 2.5 dimensional convolutional neural network system for segmenting stroke lesions in brain MRI images.

NeuroImage. Clinical
Automatic identification of brain lesions from magnetic resonance imaging (MRI) scans of stroke survivors would be a useful aid in patient diagnosis and treatment planning. It would also greatly facilitate the study of brain-behavior relationships by...

Comprehensive Exploration of Target-specific Ligands Using a Graph Convolution Neural Network.

Molecular informatics
Machine learning approaches are widely used to evaluate ligand activities of chemical compounds toward potential target proteins. Especially, exploration of highly selective ligands is important for the development of new drugs with higher safety. On...

Prediction and Interpretable Visualization of Retrosynthetic Reactions Using Graph Convolutional Networks.

Journal of chemical information and modeling
Recently, many research groups have been addressing data-driven approaches for (retro)synthetic reaction prediction and retrosynthetic analysis. Although the performances of the data-driven approach have progressed because of recent advances of machi...

Representation learning in intraoperative vital signs for heart failure risk prediction.

BMC medical informatics and decision making
BACKGROUND: The probability of heart failure during the perioperative period is 2% on average and it is as high as 17% when accompanied by cardiovascular diseases in China. It has been the most significant cause of postoperative death of patients. Ho...