AIMC Topic: Male

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Use of Machine Learning Classifiers and Sensor Data to Detect Neurological Deficit in Stroke Patients.

Journal of medical Internet research
BACKGROUND: The pronator drift test (PDT), a neurological examination, is widely used in clinics to measure motor weakness of stroke patients.

EEG-Based Computer Aided Diagnosis of Autism Spectrum Disorder Using Wavelet, Entropy, and ANN.

BioMed research international
Autism spectrum disorder (ASD) is a type of neurodevelopmental disorder with core impairments in the social relationships, communication, imagination, or flexibility of thought and restricted repertoire of activity and interest. In this work, a new c...

Multi-center machine learning in imaging psychiatry: A meta-model approach.

NeuroImage
One of the biggest problems in automated diagnosis of psychiatric disorders from medical images is the lack of sufficiently large samples for training. Sample size is especially important in the case of highly heterogeneous disorders such as schizoph...

Quantification of task-dependent cortical activation evoked by robotic continuous wrist joint manipulation in chronic hemiparetic stroke.

Journal of neuroengineering and rehabilitation
BACKGROUND: Cortical damage after stroke can drastically impair sensory and motor function of the upper limb, affecting the execution of activities of daily living and quality of life. Motor impairment after stroke has been thoroughly studied, howeve...

Physical human-robot interaction of an active pelvis orthosis: toward ergonomic assessment of wearable robots.

Journal of neuroengineering and rehabilitation
BACKGROUND: In human-centered robotics, exoskeletons are becoming relevant for addressing needs in the healthcare and industrial domains. Owing to their close interaction with the user, the safety and ergonomics of these systems are critical design f...

Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis.

Computers in biology and medicine
This study aimed to analyze the ability of extracting automatically generated features using deep structured algorithms in lung nodule CT image diagnosis, and compare its performance with traditional computer aided diagnosis (CADx) systems using hand...

Automatic migraine classification via feature selection committee and machine learning techniques over imaging and questionnaire data.

BMC medical informatics and decision making
BACKGROUND: Feature selection methods are commonly used to identify subsets of relevant features to facilitate the construction of models for classification, yet little is known about how feature selection methods perform in diffusion tensor images (...

Depressive Symptoms and Their Interactions With Emotions and Personality Traits Over Time: Interaction Networks in a Psychiatric Clinic.

The primary care companion for CNS disorders
OBJECTIVE: Associations between depression, personality traits, and emotions are complex and reciprocal. The aim of this study is to explore these interactions in dynamical networks and in a linear way over time depending on the severity of depressio...

Learning from label proportions in brain-computer interfaces: Online unsupervised learning with guarantees.

PloS one
OBJECTIVE: Using traditional approaches, a brain-computer interface (BCI) requires the collection of calibration data for new subjects prior to online use. Calibration time can be reduced or eliminated e.g., by subject-to-subject transfer of a pre-tr...