AIMC Topic: Machine Learning

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The neural representation of abstract words may arise through grounding word meaning in language itself.

Human brain mapping
In order to describe how humans represent meaning in the brain, one must be able to account for not just concrete words but, critically, also abstract words, which lack a physical referent. Hebbian formalism and optimization are basic principles of b...

Machine learning to analyze single-case graphs: A comparison to visual inspection.

Journal of applied behavior analysis
Behavior analysts commonly use visual inspection to analyze single-case graphs, but studies on its reliability have produced mixed results. To examine this issue, we compared the Type I error rate and power of visual inspection with a novel approach-...

Ensemble learning application to discover new trypanothione synthetase inhibitors.

Molecular diversity
Trypanosomatid-caused diseases are among the neglected infectious diseases with the highest disease burden, affecting about 27 million people worldwide and, in particular, socio-economically vulnerable populations. Trypanothione synthetase (TryS) is ...

Comparison of Feature Extraction Methods for Physiological Signals for Heat-Based Pain Recognition.

Sensors (Basel, Switzerland)
While even the most common definition of pain is under debate, pain assessment has remained the same for decades. But the paramount importance of precise pain management for successful healthcare has encouraged initiatives to improve the way pain is ...

Prediction of well-being and insight into work-life integration among physicians using machine learning approach.

PloS one
There has been increasing interest in examining physician well-being and its predictive factors. However, few studies have revealed the characteristics associated with physician well-being and work-life integration using a machine learning approach. ...

Bayesian Modeling for the Detection of Adverse Events Underreporting in Clinical Trials.

Drug safety
INTRODUCTION: Safety underreporting is a recurrent issue in clinical trials that can impact patient safety and data integrity. Clinical quality assurance (QA) practices used to detect underreporting rely on on-site audits; however, adverse events (AE...

Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning.

Chemical reviews
Transition-metal complexes are attractive targets for the design of catalysts and functional materials. The behavior of the metal-organic bond, while very tunable for achieving target properties, is challenging to predict and necessitates searching a...