AIMC Topic: Databases, Factual

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Using Machine Learning Applied to Real-World Healthcare Data for Predictive Analytics: An Applied Example in Bariatric Surgery.

Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
OBJECTIVES: Laparoscopic metabolic surgery (MxS) can lead to remission of type 2 diabetes (T2D); however, treatment response to MxS can be heterogeneous. Here, we demonstrate an open-source predictive analytics platform that applies machine-learning ...

A Machine Learning-Based Predictive Model of Return to Work After Sick Leave.

Journal of occupational and environmental medicine
OBJECTIVE: This study aims to build a predictive model for "return to work" (RTW) after sick leave by using a machine-learning algorithm.

A Systems Toxicology Approach for the Prediction of Kidney Toxicity and Its Mechanisms In Vitro.

Toxicological sciences : an official journal of the Society of Toxicology
The failure to predict kidney toxicity of new chemical entities early in the development process before they reach humans remains a critical issue. Here, we used primary human kidney cells and applied a systems biology approach that combines multidim...

Artificial Intelligence Within Pharmacovigilance: A Means to Identify Cognitive Services and the Framework for Their Validation.

Pharmaceutical medicine
INTRODUCTION: Pharmacovigilance (PV) detects, assesses, and prevents adverse events (AEs) and other drug-related problems by collecting, evaluating, and acting upon AEs. The volume of individual case safety reports (ICSRs) increases yearly, but it is...

The Residual Center of Mass: An Image Descriptor for the Diagnosis of Alzheimer Disease.

Neuroinformatics
A crucial quest in neuroimaging is the discovery of image features (biomarkers) associated with neurodegenerative disorders. Recent works show that such biomarkers can be obtained by image analysis techniques. However, these techniques cannot be dire...

Determinants of In-Hospital Mortality After Percutaneous Coronary Intervention: A Machine Learning Approach.

Journal of the American Heart Association
Background The ability to accurately predict the occurrence of in-hospital death after percutaneous coronary intervention is important for clinical decision-making. We sought to utilize the New York Percutaneous Coronary Intervention Reporting System...

[Palm vein recognition based on end-to-end convolutional neural network].

Nan fang yi ke da xue xue bao = Journal of Southern Medical University
We propose a novel palm-vein recognition model based on the end-to-end convolutional neural network. In this model, the convolutional layer and the pooling layer were alternately connected to extract the image features, and the categorical attribute ...

Recalibrating Fully Convolutional Networks With Spatial and Channel "Squeeze and Excitation" Blocks.

IEEE transactions on medical imaging
In a wide range of semantic segmentation tasks, fully convolutional neural networks (F-CNNs) have been successfully leveraged to achieve the state-of-the-art performance. Architectural innovations of F-CNNs have mainly been on improving spatial encod...

Segmentation of Nuclei in Histopathology Images by Deep Regression of the Distance Map.

IEEE transactions on medical imaging
The advent of digital pathology provides us with the challenging opportunity to automatically analyze whole slides of diseased tissue in order to derive quantitative profiles that can be used for diagnosis and prognosis tasks. In particular, for the ...

Predicting electrical storms by remote monitoring of implantable cardioverter-defibrillator patients using machine learning.

Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology
AIMS: Electrical storm (ES) is a serious arrhythmic syndrome that is characterized by recurrent episodes of ventricular arrhythmias. Electrical storm is associated with increased mortality and morbidity despite the use of implantable cardioverter-def...