AIMC Topic: Databases, Factual

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Assessing stroke severity using electronic health record data: a machine learning approach.

BMC medical informatics and decision making
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...

HS-CNN: a CNN with hybrid convolution scale for EEG motor imagery classification.

Journal of neural engineering
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...

[Artificial intelligence: Guidelines for internists].

La Revue de medecine interne
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...

Rule-based and machine learning algorithms identify patients with systemic sclerosis accurately in the electronic health record.

Arthritis research & therapy
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...

DeepFHR: intelligent prediction of fetal Acidemia using fetal heart rate signals based on convolutional neural network.

BMC medical informatics and decision making
BACKGROUND: Fetal heart rate (FHR) monitoring is a screening tool used by obstetricians to evaluate the fetal state. Because of the complexity and non-linearity, a visual interpretation of FHR signals using common guidelines usually results in signif...

Long Short-Term Memory Recurrent Neural Networks for Multiple Diseases Risk Prediction by Leveraging Longitudinal Medical Records.

IEEE journal of biomedical and health informatics
Individuals suffer from chronic diseases without being identified in time, which brings lots of burden of disease to the society. This paper presents a multiple disease risk prediction method to systematically assess future disease risks for patients...

Attention by Selection: A Deep Selective Attention Approach to Breast Cancer Classification.

IEEE transactions on medical imaging
Deep learning approaches are widely applied to histopathological image analysis due to the impressive levels of performance achieved. However, when dealing with high-resolution histopathological images, utilizing the original image as input to the de...

Gait Biomarkers Classification by Combining Assembled Algorithms and Deep Learning: Results of a Local Study.

Computational and mathematical methods in medicine
Machine learning, one of the core disciplines of artificial intelligence, is an approach whose main emphasis is analytical model building. In other words, machine learning enables an automaton to make its own decisions based on a previous training pr...