AIMC Topic: Deep Learning

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Adverse Drug Event Detection from Electronic Health Records Using Hierarchical Recurrent Neural Networks with Dual-Level Embedding.

Drug safety
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 and Natural Language Processing].

Brain and nerve = Shinkei kenkyu no shinpo
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...

[Medical Application of Artificial Intelligence/Deep Learning].

Brain and nerve = Shinkei kenkyu no shinpo
Deep learning is a subset of the medical application of artificial intelligence. Its significant results are garnering attention, particularly in radiographic image interpretation, pathological diagnosis, gene analysis, and prediction of cancer recur...

A Multi-View Deep Learning Framework for EEG Seizure Detection.

IEEE journal of biomedical and health informatics
The recent advances in pervasive sensing technologies have enabled us to monitor and analyze the multi-channel electroencephalogram (EEG) signals of epilepsy patients to prevent serious outcomes caused by epileptic seizures. To avoid manual visual in...

Tripartite Network-Based Repurposing Method Using Deep Learning to Compute Similarities for Drug-Target Prediction.

Methods in molecular biology (Clifton, N.J.)
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...