AIMC Topic:
Databases, Factual

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Classification of Interstitial Lung Abnormality Patterns with an Ensemble of Deep Convolutional Neural Networks.

Scientific reports Jan 15, 2020
Subtle interstitial changes in the lung parenchyma of smokers, known as Interstitial Lung Abnormalities (ILA), have been associated with clinical outcomes, including mortality, even in the absence of Interstitial Lung Disease (ILD). Although several ...
Radiographic Image Interpretation, Computer-Assisted Lung Diseases, Interstitial Tomography, X-Ray Computed Lung ROC Curve Area Under Curve Humans Neural Networks, Computer Databases, Factual Prognosis
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Prediction of progression from pre-diabetes to diabetes: Development and validation of a machine learning model.

Diabetes/metabolism research and reviews Jan 14, 2020
AIMS: Identification, a priori, of those at high risk of progression from pre-diabetes to diabetes may enable targeted delivery of interventional programmes while avoiding the burden of prevention and treatment in those at low risk. We studied whethe...
Patient Selection Israel United Kingdom Prediabetic State Time Factors Canada Diabetes Mellitus Follow-Up Studies Middle Aged Risk Assessment Disease Progression Cohort Studies Prognosis Electronic Health Records Female Humans Risk Factors Aged Machine Learning Aged, 80 and over Databases, Factual Adult Male
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A convolutional neural network outperforming state-of-the-art sleep staging algorithms for both preterm and term infants.

Journal of neural engineering Jan 14, 2020
OBJECTIVE: To classify sleep states using electroencephalogram (EEG) that reliably works over a wide range of preterm ages, as well as term age.
Electroencephalography Markov Chains Humans Infant, Premature Neural Networks, Computer Normal Distribution Algorithms Databases, Factual Brain Infant, Newborn Sleep Stages
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Automated screening of natural language in electronic health records for the diagnosis septic shock is feasible and outperforms an approach based on explicit administrative codes.

Journal of critical care Jan 9, 2020
PURPOSE: Identification of patients for epidemiologic research through administrative coding has important limitations. We investigated the feasibility of a search based on natural language processing (NLP) on the text sections of electronic health r...
Hospitalization Young Adult Adult Sensitivity and Specificity Middle Aged Incidence Shock, Septic Predictive Value of Tests Prospective Studies International Classification of Diseases Humans Intensive Care Units Natural Language Processing Software Electronic Health Records Epidemiologic Studies Databases, Factual Female Pattern Recognition, Automated False Positive Reactions
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Objective Analysis of Neck Muscle Boundaries for Cervical Dystonia Using Ultrasound Imaging and Deep Learning.

IEEE journal of biomedical and health informatics Jan 9, 2020
OBJECTIVE: To provide objective visualization and pattern analysis of neck muscle boundaries to inform and monitor treatment of cervical dystonia.
Humans Databases, Factual Deep Learning Ultrasonography Image Interpretation, Computer-Assisted Torticollis Female Aged Male Middle Aged Neck Muscles
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Assessing stroke severity using electronic health record data: a machine learning approach.

BMC medical informatics and decision making Jan 8, 2020
BACKGROUND: Stroke severity is an important predictor of patient outcomes and is commonly measured with the National Institutes of Health Stroke Scale (NIHSS) scores. Because these scores are often recorded as free text in physician reports, structur...
Male Middle Aged Severity of Illness Index Cohort Studies Natural Language Processing Electronic Health Records Stroke Humans Female Aged Machine Learning Aged, 80 and over Databases, Factual
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HS-CNN: a CNN with hybrid convolution scale for EEG motor imagery classification.

Journal of neural engineering Jan 6, 2020
OBJECTIVE: Electroencephalography (EEG) motor imagery classification has been widely used in healthcare applications such as mobile assistive robots and post-stroke rehabilitation. Recently, EEG motor imagery classification methods based on convoluti...
Electroencephalography Brain-Computer Interfaces Movement Imagination Humans Neural Networks, Computer Databases, Factual
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[Artificial intelligence: Guidelines for internists].

La Revue de medecine interne Dec 31, 2019
Following the emergence of open public databases and connected objects, big data and artificial intelligence are developing rapidly, especially in medicine, with many opportunities ranging from complex diagnostic assistance to real-time statistical a...
Practice Patterns, Physicians' Internal Medicine Physicians Big Data Humans Education, Medical, Continuing Artificial Intelligence Practice Guidelines as Topic Databases, Factual
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Assessing Radiology Research on Artificial Intelligence: A Brief Guide for Authors, Reviewers, and Readers-From the Editorial Board.

Radiology Dec 31, 2019
Humans Image Interpretation, Computer-Assisted Manuscripts, Medical as Topic Artificial Intelligence Databases, Factual Biomedical Research Radiography Radiology
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Rule-based and machine learning algorithms identify patients with systemic sclerosis accurately in the electronic health record.

Arthritis research & therapy Dec 30, 2019
BACKGROUND: Systemic sclerosis (SSc) is a rare disease with studies limited by small sample sizes. Electronic health records (EHRs) represent a powerful tool to study patients with rare diseases such as SSc, but validated methods are needed. We devel...
Scleroderma, Systemic Algorithms Sensitivity and Specificity Databases, Factual Reproducibility of Results Humans Male Electronic Health Records Aged Machine Learning Middle Aged Female Aged, 80 and over International Classification of Diseases Adult
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