OBJECTIVE: We study the performance of machine learning (ML) methods, including neural networks (NNs), to extract mutational test results from pathology reports collected by cancer registries. Given the lack of hand-labeled datasets for mutational te...
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Aug 8, 2019
In this paper, we propose a machine learning-based virtual physical therapist (PT) system to enable personalized remote training for patients with Parkinson's disease (PD). Three physical therapy tasks with multiple difficulty levels are selected to ...
Sensor-based human activity recognition (HAR) has attracted interest both in academic and applied fields, and can be utilized in health-related areas, fitness, sports training, etc. With a view to improving the performance of sensor-based HAR and opt...
BACKGROUND: Emerging evidences show that microRNA (miRNA) plays an important role in many human complex diseases. However, considering the inherent time-consuming and expensive of traditional in vitro experiments, more and more attention has been pai...
The electrocardiogram is the most widely used diagnostic tool that records the electrical activity of the heart and, therefore, its use for identifying markers for early diagnosis and detection is of paramount importance. In the last years, the huge ...
Due to its simplicity and low cost, analyzing an electrocardiogram (ECG) is the most common technique for detecting cardiac arrhythmia. The massive amount of ECG data collected every day, in home and hospital, may preclude data review by human operat...
IEEE journal of biomedical and health informatics
Aug 7, 2019
Abnormalities in pupillary light reflex can indicate optic nerve disorders that may lead to permanent visual loss if not diagnosed in an early stage. In this study, we focus on relative afferent pupillary defect (RAPD), which is based on the differen...
International journal of computer assisted radiology and surgery
Aug 7, 2019
PURPOSE: In this paper, we propose to apply generative adversarial neural networks trained with a cycle consistency loss, or CycleGANs, to improve realism in ultrasound (US) simulation from computed tomography (CT) scans.
PURPOSE: Knee osteoarthritis (KOA) is characterized by pain and decreased gait function. This study assessed key features that can be used as mechanical biomarkers for KOA severity and progression. The identified features were validated statistically...
This editorial comment refers to the article "Identification of suspicious invasive placentation based on clinical MRI data using textural features and automated machine learning" by Sun et al. in European Radiology. KEY POINTS: • Understanding how t...
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