AIMC Topic: Neural Networks, Computer

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Artificial Intelligence and Radiology in Singapore: Championing a New Age of Augmented Imaging for Unsurpassed Patient Care.

Annals of the Academy of Medicine, Singapore Jan 1, 2019
Artificial intelligence (AI) has been positioned as being the most important recent advancement in radiology, if not the most potentially disruptive. Singapore radiologists have been quick to embrace this technology as part of the natural progression...
Radiology Artificial Intelligence Singapore Machine Learning Societies, Medical Image Processing, Computer-Assisted Humans Neural Networks, Computer
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Convolutional neural network for detecting odontocete echolocation clicks.

The Journal of the Acoustical Society of America Jan 1, 2019
In this work, a convolutional neural network based method is proposed to automatically detect odontocetes echolocation clicks by analyzing acoustic data recordings from a passive acoustic monitoring system. The neural network was trained to distingui...
Neural Networks, Computer Echolocation Animals Vocalization, Animal Cetacea
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Machine learning in multiexposure laser speckle contrast imaging can replace conventional laser Doppler flowmetry.

Journal of biomedical optics Jan 1, 2019
Laser speckle contrast imaging (LSCI) enables video rate imaging of blood flow. However, its relation to tissue blood perfusion is nonlinear and depends strongly on exposure time. By contrast, the perfusion estimate from the slower laser Doppler flow...
Laser-Doppler Flowmetry Stochastic Processes Microcirculation Calibration Monte Carlo Method Blood Flow Velocity Perfusion Erythrocytes Lasers Neural Networks, Computer Image Processing, Computer-Assisted Machine Learning Models, Statistical Computer Simulation Regional Blood Flow Reproducibility of Results Humans Adult Male
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Adverse Drug Events Detection in Clinical Notes by Jointly Modeling Entities and Relations Using Neural Networks.

Drug safety Jan 1, 2019
BACKGROUND AND SIGNIFICANCE: Adverse drug events (ADEs) occur in approximately 2-5% of hospitalized patients, often resulting in poor outcomes or even death. Extraction of ADEs from clinical narratives can accelerate and automate pharmacovigilance. U...
Humans Neural Networks, Computer Deep Learning Drug-Related Side Effects and Adverse Reactions Adverse Drug Reaction Reporting Systems
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Adverse Drug Event Detection from Electronic Health Records Using Hierarchical Recurrent Neural Networks with Dual-Level Embedding.

Drug safety Jan 1, 2019
INTRODUCTION: Adverse drug event (ADE) detection is a vital step towards effective pharmacovigilance and prevention of future incidents caused by potentially harmful ADEs. The electronic health records (EHRs) of patients in hospitals contain valuable...
Deep Learning Neural Networks, Computer Drug-Related Side Effects and Adverse Reactions Machine Learning Humans Electronic Health Records
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[Deep Learning and Natural Language Processing].

Brain and nerve = Shinkei kenkyu no shinpo Jan 1, 2019
The field of natural language processing (NLP) has seen rapid advances in the past several years since the introduction of deep learning techniques. A variety of NLP tasks including syntactic parsing, machine translation, and summarization can now be...
Neural Networks, Computer Deep Learning Natural Language Processing
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Tripartite Network-Based Repurposing Method Using Deep Learning to Compute Similarities for Drug-Target Prediction.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2019
The drug discovery process is conventionally regarded as resource intensive and complex. Therefore, research effort has been put into a process called drug repositioning with the use of computational methods. Similarity-based methods are common in pr...
Deep Learning Drug Repositioning Databases, Pharmaceutical Neural Networks, Computer Algorithms Drug Discovery Humans
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Using Drug Expression Profiles and Machine Learning Approach for Drug Repurposing.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2019
The cost of new drug development has been increasing, and repurposing known medications for new indications serves as an important way to hasten drug discovery. One promising approach to drug repositioning is to take advantage of machine learning (ML...
Computational Biology Drug Repositioning Workflow Transcriptome Deep Learning Genomics Supervised Machine Learning Gene Expression Regulation Databases, Pharmaceutical Humans Neural Networks, Computer Machine Learning Algorithms Reproducibility of Results Gene Expression Profiling
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A Drug Repurposing Method Based on Drug-Drug Interaction Networks and Using Energy Model Layouts.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2019
Complex network representations of reported drug-drug interactions foster computational strategies that can infer pharmacological functions which, in turn, create incentives for drug repositioning. Here, we use Gephi (a platform for complex network v...
Humans Neural Networks, Computer Software Drug Interactions Databases, Pharmaceutical Cluster Analysis Drug Repositioning Algorithms Workflow Reproducibility of Results Computational Biology
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Deep into Patient care: An automated deep learning approach for reshaping patient care in clinical setting.

Computer methods and programs in biomedicine Jan 1, 2019
Computer Systems Humans Genome-Wide Association Study Treatment Outcome Neural Networks, Computer Computer Simulation Software Reproducibility of Results Algorithms Deep Learning Programming Languages Medical Informatics Proportional Hazards Models Models, Statistical Patient Care Leukocytes
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