Journal of neurologic physical therapy : JNPT
Jul 1, 2020
BACKGROUND AND PURPOSE: Gait asymmetries are common after stroke, and often persist despite conventional rehabilitation. Robots provide training at a greater practice frequency than conventional approaches. However, prior studies of have found the tr...
Journal of the American Medical Informatics Association : JAMIA
Jul 1, 2020
OBJECTIVE: Unsupervised machine learning approaches hold promise for large-scale clinical data. However, the heterogeneity of clinical data raises new methodological challenges in feature selection, choosing a distance metric that captures biological...
OBJECTIVES: Interpretation of lung opacities in ICU supine chest radiographs remains challenging. We evaluated a prototype artificial intelligence algorithm to classify basal lung opacities according to underlying pathologies.
BACKGROUND: There is intense interest and speculation in the application of artificial intelligence (AI) to radiology. The goals of this investigation were (1) to assess thoracic radiologists' perspectives on the role and expected impact of AI in rad...
The purpose of this study was to investigate nurses' need for care robots in children's hospitals and to help develop care robots that can be used by combining robot-care, game-care, and edu-care. This study employed a mixed-methods design; 198 nurse...
Chemical communications (Cambridge, England)
Jun 18, 2020
We report for the first time the integration of ultra-high-pressure liquid chromatography-tandem mass spectrometry with machine learning for identifying fatty acid metabolite biomarkers of ischemic stroke. In particular, we develop an optimal model t...
BACKGROUND: The diagnostic performance of CT for pancreatic cancer is interpreter-dependent, and approximately 40% of tumours smaller than 2 cm evade detection. Convolutional neural networks (CNNs) have shown promise in image analysis, but the networ...
OBJECTIVE: Electrical cardioversion is frequently performed to restore sinus rhythm in patients with persistent atrial fibrillation (AF). However, AF recurs in many patients and identifying the patients who benefit from electrical cardioversion is di...
This study evaluates the performance of convolutional neural networks (CNNs) in risk stratifying the malignant potential of thyroid nodules alongside traditional methods such as American College of Radiology Thyroid Imaging Reporting and Data System ...
OBJECTIVE: Machine learning (ML) is an innovative method to analyze large and complex data sets. The aim of this study was to evaluate the use of ML to identify predictors of early postsurgical and long-term outcomes in patients treated for Cushing d...
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