AIMC Topic: Young Adult

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Comparison of supervised machine learning classification techniques in prediction of locoregional recurrences in early oral tongue cancer.

International journal of medical informatics
BACKGROUND: The proper estimate of the risk of recurrences in early-stage oral tongue squamous cell carcinoma (OTSCC) is mandatory for individual treatment-decision making. However, this remains a challenge even for experienced multidisciplinary cent...

Using machine learning to model problematic smartphone use severity: The significant role of fear of missing out.

Addictive behaviors
We examined a model of psychopathology variables, age and sex as correlates of problematic smartphone use (PSU) severity using supervised machine learning in a sample of Chinese undergraduate students. A sample of 1097 participants completed measures...

Effects of medium- and long-distance running on cardiac damage markers in amateur runners: a systematic review, meta-analysis, and metaregression.

Journal of sport and health science
BACKGROUND: To finish an endurance race, athletes perform a vigorous effort that induces the release of cardiac damage markers. There are several factors that can affect the total number of these markers, so the aim of this review was to analyze the ...

Deep learning approaches for plethysmography signal quality assessment in the presence of atrial fibrillation.

Physiological measurement
OBJECTIVE: Photoplethysmography (PPG) monitoring has been implemented in many portable and wearable devices we use daily for health and fitness tracking. Its simplicity and cost-effectiveness has enabled a variety of biomedical applications, such as ...

Prediction of Lower Limb Kinetics and Kinematics during Walking by a Single IMU on the Lower Back Using Machine Learning.

Sensors (Basel, Switzerland)
Recent studies have reported the application of artificial neural network (ANN) techniques on data of inertial measurement units (IMUs) to predict ground reaction forces (GRFs), which could serve as quantitative indicators of sports performance or re...

Predicting the individual risk of psychosis conversion in at-risk mental state (ARMS): a multivariate model reveals the influence of nonpsychotic prodromal symptoms.

European child & adolescent psychiatry
To improve the prediction of the individual risk of conversion to psychosis in UHR subjects, by considering all CAARMS' symptoms at first presentation and using a multivariate machine learning method known as logistic regression with Elastic-net shri...

Application of Machine Learning for Predicting Clinically Meaningful Outcome After Arthroscopic Femoroacetabular Impingement Surgery.

The American journal of sports medicine
BACKGROUND: Hip arthroscopy has become an important tool for surgical treatment of intra-articular hip pathology. Predictive models for clinically meaningful outcomes in patients undergoing hip arthroscopy for femoroacetabular impingement syndrome (F...

The Interaction Between Feedback Type and Learning in Routine Grasping With Myoelectric Prostheses.

IEEE transactions on haptics
While prosthetic fitting after upper-limb loss allows for restoration of motor functions, it deprives the amputee of tactile sensations that are essential for grasp control in able-bodied subjects. Therefore, it is commonly assumed that restoring the...

Mild Dehydration Identification Using Machine Learning to Assess Autonomic Responses to Cognitive Stress.

Nutrients
The feasibility of detecting mild dehydration by using autonomic responses to cognitive stress was studied. To induce cognitive stress, subjects ( = 17) performed the Stroop task, which comprised four minutes of rest and four minutes of test. Nine in...

Prediction of lower-grade glioma molecular subtypes using deep learning.

Journal of neuro-oncology
INTRODUCTION: It is useful to know the molecular subtype of lower-grade gliomas (LGG) when deciding on a treatment strategy. This study aims to diagnose this preoperatively.