In this study, a multi-stage optimization procedure is proposed to develop deep neural network models which results in a powerful deep learning pipeline called intelligent deep learning (iDeepLe). The proposed pipeline is then evaluated by a challeng...
IEEE journal of biomedical and health informatics
Sep 18, 2018
Recognizing the factors that cause stress is a crucial step toward early detection of stressors. In this regard, several studies make an effort to recognize individuals' stress using an Electroencephalogram (EEG). However, current EEG-based stress re...
In online health expert question-answering (HQA) services, it is significant to automatically determine the quality of the answers. There are two prominent challenges in this task. First, the answers are usually written in short text, which makes it ...
INTRODUCTION: Readmission from inpatient rehabilitation facilities to acute care hospitals is a serious problem. This study aims to develop a predictive model based on machine learning algorithms to identify patients at high risk of readmission.
BMC medical informatics and decision making
Sep 18, 2018
BACKGROUND: Patients suffering obstructive sleep apnea are mainly treated with continuous positive airway pressure (CPAP). Although it is a highly effective treatment, compliance with this therapy is problematic to achieve with serious consequences f...
PURPOSE: We sought to assess whether machine learning-based classification approaches can improve the classification of pancreatic tumor models relative to more simplistic analysis methods, using T relaxation, CEST, and DCE MRI.
BACKGROUND: The advantages of Robot-assisted laparoscopic prostatectomy (RARP) over open radical prostatectomy (ORP) in Prostate cancer perioperatively are well-established, but quality of life is more contentious. Increasingly, patients are utilisin...
OBJECTIVE: Many healthcare systems employ population-based risk scores to prospectively identify patients at high risk of poor outcomes, but it is unclear whether single point-in-time scores adequately represent future risk. We sought to identify and...
BACKGROUND: Multiple layers of genetic and epigenetic variability are being simultaneously explored in an increasing number of health studies. We summarize here different approaches applied in the Data Mining and Machine Learning group at the GAW20 t...
BACKGROUND: Random forest (RF) is a machine-learning method that generally works well with high-dimensional problems and allows for nonlinear relationships between predictors; however, the presence of correlated predictors has been shown to impact it...
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