AIMC Topic: Young Adult

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Mobile phone sensors and supervised machine learning to identify alcohol use events in young adults: Implications for just-in-time adaptive interventions.

Addictive behaviors
BACKGROUND: Real-time detection of drinking could improve timely delivery of interventions aimed at reducing alcohol consumption and alcohol-related injury, but existing detection methods are burdensome or impractical.

Infliximab trough levels and persistent vs transient antibodies measured early after induction predict long-term clinical remission in patients with inflammatory bowel disease.

Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver
BACKGROUND: The use of therapeutic drug monitoring has been proposed as a useful tool in the management of patients with loss of response to biological therapy in patients with inflammatory bowel disease.

Classification of hospital admissions into emergency and elective care: a machine learning approach.

Health care management science
Rising admissions from emergency departments (EDs) to hospitals are a primary concern for many healthcare systems. The issue of how to differentiate urgent admissions from non-urgent or even elective admissions is crucial. We aim to develop a model f...

Evaluation of three machine learning models for self-referral decision support on low back pain in primary care.

International journal of medical informatics
BACKGROUND: Most people experience low back pain (LBP) at least once in their life and for some patients this evolves into a chronic condition. One way to prevent acute LBP from transiting into chronic LBP, is to ensure that patients receive the righ...

Residual Convolutional Neural Network for the Determination of Status in Low- and High-Grade Gliomas from MR Imaging.

Clinical cancer research : an official journal of the American Association for Cancer Research
Isocitrate dehydrogenase () mutations in glioma patients confer longer survival and may guide treatment decision making. We aimed to predict the status of gliomas from MR imaging by applying a residual convolutional neural network to preoperative r...

Beat-to-beat estimation of stroke volume using impedance cardiography and artificial neural network.

Medical & biological engineering & computing
Impedance cardiography is a low-cost noninvasive technique, based on monitoring of the thoracic impedance, for estimation of stroke volume (SV). Impedance cardiogram (ICG) is the negative of the first derivative of the impedance signal. A technique f...

False memory for orthographically versus semantically similar words in adolescents with dyslexia: a fuzzy-trace theory perspective.

Annals of dyslexia
The presented research was conducted in order to investigate the connections between developmental dyslexia and the functioning of verbatim and gist memory traces-assumed in the fuzzy-trace theory. The participants were 71 high school students (33 wi...

Beyond modularity: Fine-scale mechanisms and rules for brain network reconfiguration.

NeuroImage
The human brain is in constant flux, as distinct areas engage in transient communication to support basic behaviors as well as complex cognition. The collection of interactions between cortical and subcortical areas forms a functional brain network w...

Image processing strategies based on saliency segmentation for object recognition under simulated prosthetic vision.

Artificial intelligence in medicine
BACKGROUND AND OBJECTIVE: Current retinal prostheses can only generate low-resolution visual percepts constituted of limited phosphenes which are elicited by an electrode array and with uncontrollable color and restricted grayscale. Under this visual...

Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms.

Physics in medicine and biology
Transfer learning in deep convolutional neural networks (DCNNs) is an important step in its application to medical imaging tasks. We propose a multi-task transfer learning DCNN with the aim of translating the 'knowledge' learned from non-medical imag...