AIMC Topic: Adolescent

Clear Filters Showing 2621 to 2630 of 3540 articles

Tele-Manipulation with Two Asymmetric Slaves: Two Operators Perform Better Than One.

IEEE transactions on haptics
Certain tele-manipulation tasks require manipulation by two asymmetric slaves, for example, a crane for hoisting and a dexterous robotic arm for fine manipulation. It is unclear how to best design human-in-the-loop control over two asymmetric slaves....

Using Temporal Sensitivity to Predict Performance Under Latency in Teleoperation.

Human factors
Objective This article establishes a relationship between temporal sensitivity and task performance under one-way latency between input and response. Background As the latency between human input and telerobot response increases, performance (e.g., s...

Artificial neural network coding of the child attachment interview using linguistic data.

Attachment & human development
Assessing attachment in adolescents is important due to relations between insecurity and psychopathology. The child attachment interview (CAI) holds promise in this regard, but is time-consuming to code, which may render it inaccessible. The aim of t...

Deep Neural Networks for the Recognition and Classification of Heart Murmurs Using Neuromorphic Auditory Sensors.

IEEE transactions on biomedical circuits and systems
Auscultation is one of the most used techniques for detecting cardiovascular diseases, which is one of the main causes of death in the world. Heart murmurs are the most common abnormal finding when a patient visits the physician for auscultation. The...

Detection of Interictal Discharges With Convolutional Neural Networks Using Discrete Ordered Multichannel Intracranial EEG.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Detection algorithms for electroencephalography (EEG) data, especially in the field of interictal epileptiform discharge (IED) detection, have traditionally employed handcrafted features, which utilized specific characteristics of neural responses. A...

Validation of a Machine Learning Approach for Venous Thromboembolism Risk Prediction in Oncology.

Disease markers
Using kernel machine learning (ML) and random optimization (RO) techniques, we recently developed a set of venous thromboembolism (VTE) risk predictors, which could be useful to devise a web interface for VTE risk stratification in chemotherapy-treat...

Computerized Bone Age Estimation Using Deep Learning Based Program: Evaluation of the Accuracy and Efficiency.

AJR. American journal of roentgenology
OBJECTIVE: The purpose of this study is to evaluate the accuracy and efficiency of a new automatic software system for bone age assessment and to validate its feasibility in clinical practice.

[Evaluation of equations using cystatin C for estimation of the glomerular filtration rate in healthy adult population of canidates for kidney donors.].

Revista de la Facultad de Ciencias Medicas (Cordoba, Argentina)
The determination of the glomerular filtration rate (GFR) is critical for the selection of potential kidney donors. Methods of measurement of GFR are impractical and complex, which led to development of equations to estimate GFR. Objective: To evalua...

Automatic machine-learning based identification of jogging periods from accelerometer measurements of adolescents under field conditions.

PloS one
BACKGROUND: Assessment of health benefits associated with physical activity depend on the activity duration, intensity and frequency, therefore their correct identification is very valuable and important in epidemiological and clinical studies. The a...

A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme.

Scientific reports
Traditional radiomics models mainly rely on explicitly-designed handcrafted features from medical images. This paper aimed to investigate if deep features extracted via transfer learning can generate radiomics signatures for prediction of overall sur...