AIMC Topic: Humans

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A modeling and machine learning approach to ECG feature engineering for the detection of ischemia using pseudo-ECG.

PloS one
Early detection of coronary heart disease (CHD) has the potential to prevent the millions of deaths that this disease causes worldwide every year. However, there exist few automatic methods to detect CHD at an early stage. A challenge in the developm...

An investigation of CNN models for differentiating malignant from benign lesions using small pathologically proven datasets.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Cancer has been one of the most threatening diseases to human health. There have been many efforts devoted to the advancement of radiology and transformative tools (e.g. non-invasive computed tomographic or CT imaging) to detect cancer in early stage...

On strategic choices faced by large pharmaceutical laboratories and their effect on innovation risk under fuzzy conditions.

Artificial intelligence in medicine
OBJECTIVES: We develop a fuzzy evaluation model that provides managers at different responsibility levels in pharmaceutical laboratories with a rich picture of their innovation risk as well as that of competitors. This would help them take better str...

Early prediction of epileptic seizures using a long-term recurrent convolutional network.

Journal of neuroscience methods
BACKGROUND: A seizure prediction system can detect seizures prior to their occurrence and allow clinicians to provide timely treatment for patients with epilepsy. Research on seizure prediction has progressed from signal processing analyses to machin...

Knowledge-based and deep learning-based automated chest wall segmentation in magnetic resonance images of extremely dense breasts.

Medical physics
PURPOSE: Segmentation of the chest wall, is an important component of methods for automated analysis of breast magnetic resonance imaging (MRI). Methods reported to date show promising results but have difficulties delineating the muscle border corre...

Intelligent Imaging: Artificial Intelligence Augmented Nuclear Medicine.

Journal of nuclear medicine technology
Artificial intelligence (AI) in nuclear medicine and radiology represents a significant disruptive technology. Although there has been much debate about the impact of AI on the careers of radiologists, the opportunities in nuclear medicine enhance th...

Training machine learning models to predict 30-day mortality in patients discharged from the emergency department: a retrospective, population-based registry study.

BMJ open
OBJECTIVES: The aim of this work was to train machine learning models to identify patients at end of life with clinically meaningful diagnostic accuracy, using 30-day mortality in patients discharged from the emergency department (ED) as a proxy.

Constructing a Personalized Cross-Day EEG-Based Emotion-Classification Model Using Transfer Learning.

IEEE journal of biomedical and health informatics
State-of-the-art electroencephalogram (EEG)-based emotion-classification works indicate that a personalized model may not be well exploited until sufficient labeled data are available, given a substantial EEG non-stationarity over days. However, it i...

Automatic diagnosis of fungal keratitis using data augmentation and image fusion with deep convolutional neural network.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Fungal keratitis is caused by inflammation of the cornea that results from infection by fungal organisms. The lack of an early effective diagnosis often results in serious complications even blindness. Confocal microscopy i...

Automation of the Timed-Up-and-Go Test Using a Conventional Video Camera.

IEEE journal of biomedical and health informatics
The Timed-Up-and-Go (TUG) test is a simple clinical tool commonly used to quickly assess the mobility of patients. Researchers have endeavored to automate the test using sensors or motion tracking systems to improve its accuracy and to extract more r...