AIMC Topic: Machine Learning

Clear Filters Showing 13071 to 13080 of 34417 articles

Predicting long-term neurocognitive outcome after pediatric intensive care unit admission for bronchiolitis-preliminary exploration of the potential of machine learning.

European journal of pediatrics
PURPOSE: For successful prevention and intervention, it is important to unravel the complex constellation of factors that affect neurocognitive functioning after pediatric intensive care unit (PICU) admission. This study aims (1) to elucidate the pot...

ExplaiNAble BioLogical Age (ENABL Age): an artificial intelligence framework for interpretable biological age.

The lancet. Healthy longevity
BACKGROUND: Biological age is a measure of health that offers insights into ageing. The existing age clocks, although valuable, often trade off accuracy and interpretability. We introduce ExplaiNAble BioLogical Age (ENABL Age), a computational framew...

Reduction of Biosensor False Responses and Time Delay Using Dynamic Response and Theory-Guided Machine Learning.

ACS sensors
Here, we provide a new methodology for reducing false results and time delay of biosensors, which are barriers to industrial, healthcare, military, and consumer applications. We show that integrating machine learning with domain knowledge in biosensi...

Artificial intelligence: Machine learning approach for screening large database and drug discovery.

Antiviral research
Recent research in drug discovery dealing with many faces difficulties, including development of new drugs during disease outbreak and drug resistance due to rapidly accumulating mutations. Virtual screening is the most widely used method in computer...

Predicting dengue transmission rates by comparing different machine learning models with vector indices and meteorological data.

Scientific reports
Machine learning algorithms (ML) are receiving a lot of attention in the development of predictive models for monitoring dengue transmission rates. Previous work has focused only on specific weather variables and algorithms, and there is still a need...

Can machine learning provide preoperative predictions of biological hemostasis after extracorporeal circulation for cardiac surgery?

The Journal of thoracic and cardiovascular surgery
OBJECTIVES: The goal of this study was to improve decision making regarding the transfusion of patients at the end of extracorporeal circulation for cardiac surgery through machine learning predictions of the evolution of platelets counts, prothrombi...

Quantitative image signature and machine learning-based prediction of outcomes in cerebral cavernous malformations.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
PURPOSE: There is increasing interest in novel prognostic tools and predictive biomarkers to help identify, with more certainty, cerebral cavernous malformations (CCM) susceptible of bleeding if left untreated. We developed explainable quantitative-b...

Leveraging attention-enhanced variational autoencoders: Novel approach for investigating latent space of aptamer sequences.

International journal of biological macromolecules
Aptamers are increasingly recognized as potent alternatives to antibodies for diagnostic and therapeutic applications. The application of deep learning, particularly attention-based models, for aptamer (DNA/RNA) sequences is an innovative field. The ...

A Tutorial Introduction to Heterogeneous Treatment Effect Estimation with Meta-learners.

Administration and policy in mental health
Psychotherapy has been proven to be effective on average, though patients respond very differently to treatment. Understanding which characteristics are associated with treatment effect heterogeneity can help to customize therapy to the individual pa...

Can we use machine learning to improve the interpretation and application of urodynamic data?: ICI-RS 2023.

Neurourology and urodynamics
INTRODUCTION: A "Think Tank" at the International Consultation on Incontinence-Research Society meeting held in Bristol, United Kingdom in June 2023 considered the progress and promise of machine learning (ML) applied to urodynamic data.