AIMC Topic: Machine Learning

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Diagnosis of Dementia by Machine learning methods in Epidemiological studies: a pilot exploratory study from south India.

Social psychiatry and psychiatric epidemiology
BACKGROUND: There are limited data on the use of artificial intelligence methods for the diagnosis of dementia in epidemiological studies in low- and middle-income country (LMIC) settings. A culture and education fair battery of cognitive tests was d...

Identifying incipient dementia individuals using machine learning and amyloid imaging.

Neurobiology of aging
Identifying individuals destined to develop Alzheimer's dementia within time frames acceptable for clinical trials constitutes an important challenge to design studies to test emerging disease-modifying therapies. Although amyloid-β protein is the co...

Context-dependent adaptation improves robustness of myoelectric control for upper-limb prostheses.

Journal of neural engineering
OBJECTIVE: Dexterous upper-limb prostheses are available today to restore grasping, but an effective and reliable feed-forward control is still missing. The aim of this work was to improve the robustness and reliability of myoelectric control by usin...

Machine-Learning-Assisted Approach for Discovering Novel Inhibitors Targeting Bromodomain-Containing Protein 4.

Journal of chemical information and modeling
Bromodomain-containing protein 4 (BRD4) is implicated in the pathogenesis of a number of different cancers, inflammatory diseases and heart failure. Much effort has been dedicated toward discovering novel scaffold BRD4 inhibitors (BRD4is) with differ...

Assessing Suicide Risk and Emotional Distress in Chinese Social Media: A Text Mining and Machine Learning Study.

Journal of medical Internet research
BACKGROUND: Early identification and intervention are imperative for suicide prevention. However, at-risk people often neither seek help nor take professional assessment. A tool to automatically assess their risk levels in natural settings can increa...

Machine learning methods to predict child posttraumatic stress: a proof of concept study.

BMC psychiatry
BACKGROUND: The care of traumatized children would benefit significantly from accurate predictive models for Posttraumatic Stress Disorder (PTSD), using information available around the time of trauma. Machine Learning (ML) computational methods have...

Machine learning-enabled discovery and design of membrane-active peptides.

Bioorganic & medicinal chemistry
Antimicrobial peptides are a class of membrane-active peptides that form a critical component of innate host immunity and possess a diversity of sequence and structure. Machine learning approaches have been profitably employed to efficiently screen s...

Automatic classification of RDoC positive valence severity with a neural network.

Journal of biomedical informatics
OBJECTIVE: Our objective was to develop a machine learning-based system to determine the severity of Positive Valance symptoms for a patient, based on information included in their initial psychiatric evaluation. Severity was rated on an ordinal scal...

Stability and synchronization of fractional-order memristive neural networks with multiple delays.

Neural networks : the official journal of the International Neural Network Society
The paper presents theoretical results on the global asymptotic stability and synchronization of a class of fractional-order memristor-based neural networks (FMNN) with multiple delays. First, the asymptotic stability of fractional-order (FO) linear ...

Overview of deep learning in medical imaging.

Radiological physics and technology
The use of machine learning (ML) has been increasing rapidly in the medical imaging field, including computer-aided diagnosis (CAD), radiomics, and medical image analysis. Recently, an ML area called deep learning emerged in the computer vision field...