Previous studies have demonstrated that cardiorespiratory fitness is a strong marker of cardiovascular health. Machine learning (ML) can enhance the prediction of outcomes through classification techniques that classify the data into predetermined ca...
Lung disease risk stratification is important for both diagnosis and treatment planning, particularly in biopsies and radiation therapy. Manual lung disease risk stratification is challenging because of: (a) large lung data sizes, (b) inter- and intr...
This study investigated the prognostic value of coronary computed tomography angiography (cCTA)-derived fractional flow reserve (CT-FFR) in patients with acute coronary syndrome (ACS) and multivessel disease to gauge significance and guide management...
IEEE transactions on bio-medical engineering
Jul 24, 2017
OBJECTIVE: Acute coronary syndrome (ACS), as a common and severe cardiovascular disease, is a leading cause of death and the principal cause of serious long-term disability globally. Clinical risk prediction of ACS is important for early intervention...
INTRODUCTION: To compare the pre- and post-operative Gleason scores (GS) in patients with localized prostate cancer treated with robot-assisted laparoscopic radical prostatectomy.
The identification of indicators for severe HFMD is critical for early prevention and control of the disease. With this goal in mind, 185 severe and 345 mild HFMD cases were assessed. Patient demographics, clinical features, MRI findings, and laborat...
BMC medical informatics and decision making
Jul 5, 2017
BACKGROUND: Machine learning algorithms hold potential for improved prediction of all-cause mortality in cardiovascular patients, yet have not previously been developed with high-quality population data. This study compared four popular machine learn...
OBJECTIVE: Predict the number of hospitalizations for asthma and pneumonia associated with exposure to air pollutants in the city of São José dos Campos, São Paulo State.
It is important to identify and prevent disease risk as early as possible through regular physical examinations. We formulate the disease risk prediction into a multilabel classification problem. A novel Ensemble Label Power-set Pruned datasets Joint...
We aimed to identify optimal machine-learning methods for radiomics-based prediction of local failure and distant failure in advanced nasopharyngeal carcinoma (NPC). We enrolled 110 patients with advanced NPC. A total of 970 radiomic features were ex...
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