AIMC Topic: Machine Learning

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Artificial Intelligence in Oncology: Current Capabilities, Future Opportunities, and Ethical Considerations.

American Society of Clinical Oncology educational book. American Society of Clinical Oncology. Annual Meeting
The promise of highly personalized oncology care using artificial intelligence (AI) technologies has been forecasted since the emergence of the field. Cumulative advances across the science are bringing this promise to realization, including refineme...

Machine Learning Applied to Routinely Collected Health Administrative Data.

Healthcare quarterly (Toronto, Ont.)
There has been considerable growth in the development of machine learning algorithms for clinical applications. The authors survey recent machine learning models developed with the use of large health administrative databases at ICES and highlight th...

Machine Learning Prediction of Non-Coding Variant Impact in Human Retinal cis-Regulatory Elements.

Translational vision science & technology
PURPOSE: Prior studies have demonstrated the significance of specific cis-regulatory variants in retinal disease; however, determining the functional impact of regulatory variants remains a major challenge. In this study, we utilized a machine learni...

A proposal for developing a platform that evaluates algorithmic equity and accuracy.

BMJ health & care informatics
We are at a pivotal moment in the development of healthcare artificial intelligence (AI), a point at which enthusiasm for machine learning has not caught up with the scientific evidence to support the equity and accuracy of diagnostic and therapeutic...

Prediction of Subjective Refraction From Anterior Corneal Surface, Eye Lengths, and Age Using Machine Learning Algorithms.

Translational vision science & technology
PURPOSE: To develop a machine learning regression model of subjective refractive prescription from minimum ocular biometry and corneal topography features.

Resampling to address inequities in predictive modeling of suicide deaths.

BMJ health & care informatics
OBJECTIVE: Improve methodology for equitable suicide death prediction when using sensitive predictors, such as race/ethnicity, for machine learning and statistical methods.

Can medical algorithms be fair? Three ethical quandaries and one dilemma.

BMJ health & care informatics
OBJECTIVE: To demonstrate what it takes to reconcile the idea of fairness in medical algorithms and machine learning (ML) with the broader discourse of fairness and health equality in health research.

Machine learning to predict passenger mortality and hospital length of stay following motor vehicle collision.

Neurosurgical focus
OBJECTIVE: Motor vehicle collisions (MVCs) account for 1.35 million deaths and cost $518 billion US dollars each year worldwide, disproportionately affecting young patients and low-income nations. The ability to successfully anticipate clinical outco...

Five points to consider when reading a translational machine-learning paper.

The British journal of psychiatry : the journal of mental science
Machine-learning techniques are used in this BJPsych special issue on precision medicine in attempts to create statistical models that make clinically relevant predictions for individual patients. In this primer, we outline five key points that are h...