AIMC Topic: Databases, Factual

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Development of an artificial intelligence system to classify pathology and clinical features on retinal fundus images.

Clinical & experimental ophthalmology
IMPORTANCE: Artificial intelligence (AI) algorithms are under development for use in diabetic retinopathy photo screening pathways. To be clinically acceptable, such systems must also be able to classify other fundus abnormalities and clinical featur...

Synthesizing Chest X-Ray Pathology for Training Deep Convolutional Neural Networks.

IEEE transactions on medical imaging
Medical datasets are often highly imbalanced with over-representation of prevalent conditions and poor representation of rare medical conditions. Due to privacy concerns, it is challenging to aggregate large datasets between health care institutions....

Exploring Active Learning Based on Representativeness and Uncertainty for Biomedical Data Classification.

IEEE journal of biomedical and health informatics
Nowadays, there is an abundance of biomedical data, such as images and genetic sequences, among others. However, there is a lack of annotation to such volume of data, due to the high costs involved to perform this task. Thus, it is mandatory to devel...

Kafnets: Kernel-based non-parametric activation functions for neural networks.

Neural networks : the official journal of the International Neural Network Society
Neural networks are generally built by interleaving (adaptable) linear layers with (fixed) nonlinear activation functions. To increase their flexibility, several authors have proposed methods for adapting the activation functions themselves, endowing...

Transforming health policy through machine learning.

PLoS medicine
In their Perspective, Ara Darzi and Hutan Ashrafian give us a tour of the future policymaker's machine learning toolkit.

Applying machine learning to continuously monitored physiological data.

Journal of clinical monitoring and computing
The use of machine learning (ML) in healthcare has enormous potential for improving disease detection, clinical decision support, and workflow efficiencies. In this commentary, we review published and potential applications for the use of ML for moni...

Multiple Human-Behaviour Indicators for Predicting Lung Cancer Mortality with Support Vector Machine.

Scientific reports
Lung cancer is still one of the most common causes of death around the world, while there is overwhelming evidence that the environment and lifestyle factors are predominant causes of most sporadic cancers. However, when applying human-behaviour indi...

Prediction of Farnesoid X Receptor Disruptors with Machine Learning Methods.

Chemical research in toxicology
The farnesoid X receptor (FXR) emerges as a promising drug target involved in regulating various metabolic pathways, yet some xenobiotic compounds binding to FXR would be an important determinant to induce the receptor dysfunctions that lead to undes...

Chest Radiographs in Congestive Heart Failure: Visualizing Neural Network Learning.

Radiology
Purpose To examine Generative Visual Rationales (GVRs) as a tool for visualizing neural network learning of chest radiograph features in congestive heart failure (CHF). Materials and Methods A total of 103 489 frontal chest radiographs in 46 712 pati...

Machine learning predicts individual cancer patient responses to therapeutic drugs with high accuracy.

Scientific reports
Precision or personalized cancer medicine is a clinical approach that strives to customize therapies based upon the genomic profiles of individual patient tumors. Machine learning (ML) is a computational method particularly suited to the establishmen...