AIMC Topic: Young Adult
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Self-Anamnesis with a Conversational User Interface: Concept and Usability Study.
Methods of information in medicine
Mar 15, 2019
OBJECTIVE: Self-anamnesis is a procedure in which a patient answers questions about the personal medical history without interacting directly with a doctor or medical assistant. If collected digitally, the anamnesis data can be shared among the healt...
A Generic Human-Machine Annotation Framework Based on Dynamic Cooperative Learning.
IEEE transactions on cybernetics
Mar 14, 2019
The task of obtaining meaningful annotations is a tedious work, incurring considerable costs and time consumption. Dynamic active learning and cooperative learning are recently proposed approaches to reduce human effort of annotating data with subjec...
Visual network alterations in brain functional connectivity in chronic low back pain: A resting state functional connectivity and machine learning study.
NeuroImage. Clinical
Mar 14, 2019
Chronic low back pain (cLBP) is associated with widespread functional and structural changes in the brain. This study aims to investigate the resting state functional connectivity (rsFC) changes of visual networks in cLBP patients and the feasibility...
Deep Collaborative Learning With Application to the Study of Multimodal Brain Development.
IEEE transactions on bio-medical engineering
Mar 13, 2019
OBJECTIVE: Multi-modal functional magnetic resonance imaging has been widely used for brain research. Conventional data-fusion methods cannot capture complex relationship (e.g., nonlinear predictive relationship) between multiple data. This paper aim...
On the Relative Contribution of Deep Convolutional Neural Networks for SSVEP-Based Bio-Signal Decoding in BCI Speller Applications.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Mar 13, 2019
Brain-computer interfaces (BCI) harnessing steady state visual evoked potentials (SSVEPs) manipulate the frequency and phase of visual stimuli to generate predictable oscillations in neural activity. For BCI spellers, oscillations are matched with al...
Effects of Gender and Relationship Type on the Response to Artificial Intelligence.
Cyberpsychology, behavior and social networking
Mar 13, 2019
Artificial intelligence (AI) has had a huge impact on our lives. In this study, we suggest that when people interact with AI, they regard the AI as a social actor and apply interpersonal relationship norms. This study employed a 2 × 2 between-subject...
Development of an intelligent decision support system for ischemic stroke risk assessment in a population-based electronic health record database.
PloS one
Mar 13, 2019
BACKGROUND: Intelligent decision support systems (IDSS) have been applied to tasks of disease management. Deep neural networks (DNNs) are artificial intelligent techniques to achieve high modeling power. The application of DNNs to large-scale data fo...
Intact metabolite spectrum mining by deep learning in proton magnetic resonance spectroscopy of the brain.
Magnetic resonance in medicine
Mar 12, 2019
PURPOSE: To develop a robust method for brain metabolite quantification in proton magnetic resonance spectroscopy ( H-MRS) using a convolutional neural network (CNN) that maps in vivo brain spectra that are typically degraded by low SNR, line broaden...
Convolutional neural network to predict the local recurrence of giant cell tumor of bone after curettage based on pre-surgery magnetic resonance images.
European radiology
Mar 11, 2019
OBJECTIVE: To predict the local recurrence of giant cell bone tumors (GCTB) on MR features and the clinical characteristics after curettage using a deep convolutional neural network (CNN).
Male
Middle Aged
Adolescent
Logistic Models
Follow-Up Studies
Prognosis
Female
Young Adult
Adult
Image Interpretation, Computer-Assisted
Preoperative Period
Bone and Bones
Magnetic Resonance Imaging
Giant Cell Tumor of Bone
Humans
Bone Neoplasms
Curettage
Neural Networks, Computer
Neoplasm Staging
Algorithms
Neoplasm Recurrence, Local
Prediction of survival outcomes in patients with epithelial ovarian cancer using machine learning methods.
Journal of gynecologic oncology
Mar 11, 2019
OBJECTIVES: The aim of this study was to develop a new prognostic classification for epithelial ovarian cancer (EOC) patients using gradient boosting (GB) and to compare the accuracy of the prognostic model with the conventional statistical method.
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