AIMC Topic: Deep Learning

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[A contemporary review of deep learning in otorhinolaryngology-head and neck surgery].

Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery Oct 7, 2020
Humans Neck Otolaryngology Deep Learning
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Prediction of systemic biomarkers from retinal photographs: development and validation of deep-learning algorithms.

The Lancet. Digital health Oct 1, 2020
BACKGROUND: The application of deep learning to retinal photographs has yielded promising results in predicting age, sex, blood pressure, and haematological parameters. However, the broader applicability of retinal photograph-based deep learning for ...
Europe Asia Area Under Curve Photography Republic of Korea Muscles Singapore United Kingdom Algorithms Female ROC Curve Creatinine Beijing Humans Biomarkers Middle Aged Image Processing, Computer-Assisted Retina Body Composition Neural Networks, Computer Deep Learning Male Ethnicity Models, Biological
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Learning dynamical systems in noise using convolutional neural networks.

Chaos (Woodbury, N.Y.) Oct 1, 2020
The problem of distinguishing deterministic chaos from non-chaotic dynamics has been an area of active research in time series analysis. Since noise contamination is unavoidable, it renders deterministic chaotic dynamics corrupted by noise to appear ...
Reproducibility of Results Deep Learning Regression Analysis Nonlinear Dynamics Image Processing, Computer-Assisted Stochastic Processes
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Physics-Guided Deep Learning for Drag Force Prediction in Dense Fluid-Particulate Systems.

Big data Oct 1, 2020
Physics-based simulations are often used to model and understand complex physical systems in domains such as fluid dynamics. Such simulations, although used frequently, often suffer from inaccurate or incomplete representations either due to their hi...
Hydrodynamics Computer Simulation Neural Networks, Computer Deep Learning Data Mining
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The impact of learning Unified Medical Language System knowledge embeddings in relation extraction from biomedical texts.

Journal of the American Medical Informatics Association : JAMIA Oct 1, 2020
OBJECTIVE: We explored how knowledge embeddings (KEs) learned from the Unified Medical Language System (UMLS) Metathesaurus impact the quality of relation extraction on 2 diverse sets of biomedical texts.
Information Storage and Retrieval Unified Medical Language System Deep Learning Knowledge Bases
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Use of word and graph embedding to measure semantic relatedness between Unified Medical Language System concepts.

Journal of the American Medical Informatics Association : JAMIA Oct 1, 2020
OBJECTIVE: The study sought to explore the use of deep learning techniques to measure the semantic relatedness between Unified Medical Language System (UMLS) concepts.
Unified Medical Language System ROC Curve Natural Language Processing Medical Subject Headings Terminology as Topic Semantics Deep Learning
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A Potential Solution to Plus Disease Variability.

Ophthalmology. Retina Oct 1, 2020
Infant, Newborn Deep Learning Retinopathy of Prematurity Humans Infant, Premature, Diseases
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High-contrast scleroconjunctival microvasculature via deep learning denoising.

Indian journal of ophthalmology Oct 1, 2020
Neural Networks, Computer Deep Learning Image Processing, Computer-Assisted Humans Microvessels
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The 2019 National Natural language processing (NLP) Clinical Challenges (n2c2)/Open Health NLP (OHNLP) shared task on clinical concept normalization for clinical records.

Journal of the American Medical Informatics Association : JAMIA Oct 1, 2020
OBJECTIVE: The 2019 National Natural language processing (NLP) Clinical Challenges (n2c2)/Open Health NLP (OHNLP) shared task track 3, focused on medical concept normalization (MCN) in clinical records. This track aimed to assess the state of the art...
Information Storage and Retrieval Unified Medical Language System Patient Discharge Summaries Deep Learning Natural Language Processing Humans Datasets as Topic
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Low-Shot Deep Learning of Diabetic Retinopathy With Potential Applications to Address Artificial Intelligence Bias in Retinal Diagnostics and Rare Ophthalmic Diseases.

JAMA ophthalmology Oct 1, 2020
IMPORTANCE: Recent studies have demonstrated the successful application of artificial intelligence (AI) for automated retinal disease diagnostics but have not addressed a fundamental challenge for deep learning systems: the current need for large, cr...
Algorithms Retrospective Studies Female Neural Networks, Computer Artificial Intelligence Deep Learning ROC Curve Rare Diseases Cross-Sectional Studies Humans Male Diabetic Retinopathy
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