AIMC Topic: Machine Learning

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Evaluating GenAI systems to combat mental health issues in healthcare workers: An integrative literature review.

International journal of medical informatics
BACKGROUND: Mental health issues among healthcare workers remain a serious problem globally. Recent surveys continue to report high levels of depression, anxiety, burnout and other conditions amongst various occupational groups. Novel approaches are ...

Off-Label use of Woven EndoBridge device for intracranial brain aneurysm treatment: Modeling of occlusion outcome.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
INTRODUCTION: The Woven EndoBridge (WEB) device is emerging as a novel therapy for intracranial aneurysms, but its use for off-label indications requires further study. Using machine learning, we aimed to develop predictive models for complete occlus...

Artificial intelligence in endodontics: Fundamental principles, workflow, and tasks.

International endodontic journal
The integration of artificial intelligence (AI) in healthcare has seen significant advancements, particularly in areas requiring image interpretation. Endodontics, a specialty within dentistry, stands to benefit immensely from AI applications, especi...

Machine Learning Methods to Estimate Individualized Treatment Effects for Use in Health Technology Assessment.

Medical decision making : an international journal of the Society for Medical Decision Making
BACKGROUND: Recent developments in causal inference and machine learning (ML) allow for the estimation of individualized treatment effects (ITEs), which reveal whether treatment effectiveness varies according to patients' observed covariates. ITEs ca...

Diagnostic Performance of Machine Learning-based Models in Neonatal Sepsis: A Systematic Review.

The Pediatric infectious disease journal
BACKGROUND: Timely diagnosis of neonatal sepsis is challenging. We aimed to systematically evaluate the diagnostic performance of sophisticated machine learning (ML) techniques for the prediction of neonatal sepsis.

Interpretable machine learning guided by physical mechanisms reveals drivers of runoff under dynamic land use changes.

Journal of environmental management
Human activities continuously impact water balances and cycling in watersheds, making it essential to accurately identify the responses of runoff to dynamic changes in land use types. Although machine learning models demonstrate promise in capturing ...

Significance of Artificial Intelligence in the Study of Virus-Host Cell Interactions.

Biomolecules
A highly critical event in a virus's life cycle is successfully entering a given host. This process begins when a viral glycoprotein interacts with a target cell receptor, which provides the molecular basis for target virus-host cell interactions for...

AI-Assisted Rational Design and Activity Prediction of Biological Elements for Optimizing Transcription-Factor-Based Biosensors.

Molecules (Basel, Switzerland)
The rational design, activity prediction, and adaptive application of biological elements (bio-elements) are crucial research fields in synthetic biology. Currently, a major challenge in the field is efficiently designing desired bio-elements and acc...