Neural network-based approaches for biomedical relation classification: A review.
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TLDR
The recent advancement of neural network-based approaches for classifying biomedical relations is described, including convolutional neural networks (CNNs) and recurrent neural Networks (RNNs), and the remaining challenges are described and the future directions are outlined.About:
This article is published in Journal of Biomedical Informatics.The article was published on 2019-11-01 and is currently open access. It has received 48 citations till now. The article focuses on the topics: Deep learning & Recurrent neural network.read more
Citations
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Named Entity Recognition and Relation Detection for Biomedical Information Extraction
TL;DR: Best practices for Named Entity Recognition (NER) and Relation Detection (RD) are reviewed, allowing, e.g., to identify interactions between proteins and drugs or genes and diseases.
Journal ArticleDOI
An intelligent fuzzy inference rule‐based expert recommendation system for predictive diabetes diagnosis
P. Nagaraj,P. Deepalakshmi +1 more
TL;DR: An intelligent fuzzy inference rule‐based predictive diabetes diagnosis model (IFIR_PDDM), providing content recommendations to patients with diabetes by employing an inference technique that medical specialists have validated for recommendations.
Journal ArticleDOI
Medical Information Extraction in the Age of Deep Learning.
Udo Hahn,Michel Oleynik +1 more
TL;DR: The paradigm shift from (feature-engineered) ML to DNNs changes the fundamental methodological rules of the game for medical NLP and should also deeply influence other areas of medical informatics, either NLP- or non-NLP-based.
Journal ArticleDOI
Applications of artificial intelligence and machine learning in respiratory medicine
TL;DR: Progress in deep neural networks within respiratory medicine over the past 5 years is surveyed, highlighting the current limitations of AI and machine learning and the potential for future developments.
Journal ArticleDOI
Modular Characteristics and Mechanism of Action of Herbs for Endometriosis Treatment in Chinese Medicine: A Data Mining and Network Pharmacology–Based Identification
TL;DR: It is found that Chinese medicine could affect the development of endometriosis by regulating inflammation, immunity, angiogenesis, and other clusters of processes identified by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses.
References
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Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
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Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Proceedings Article
Neural Machine Translation by Jointly Learning to Align and Translate
TL;DR: It is conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and it is proposed to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly.
Journal ArticleDOI
A Survey on Transfer Learning
Sinno Jialin Pan,Qiang Yang +1 more
TL;DR: The relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift are discussed.
Posted Content
Neural Machine Translation by Jointly Learning to Align and Translate
TL;DR: In this paper, the authors propose to use a soft-searching model to find the parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly.