Not long ago, I participated in an exercise that asked educators to define thinking and learning. It was a familiar prompt, ...
Background: Asian Americans (AAs) experience disproportionately higher rates of intracerebral hemorrhage (ICH) compared to other populations. Accurate stroke subtype prediction remains challenging due ...
Abstract: Machine Learning Health Operations (MLHOps) is the combination of processes for reliable, efficient, usable, and ethical deployment and maintenance of machine learning models in healthcare ...
ABSTRACT: Machine learning (ML) has revolutionized risk management by enabling organizations to make data-driven decisions with higher accuracy and speed. However, as machine learning models grow more ...
ABSTRACT: Machine learning (ML) has revolutionized risk management by enabling organizations to make data-driven decisions with higher accuracy and speed. However, as machine learning models grow more ...
Abstract: Machine learning models for continuous outcomes often yield systematically biased predictions, particularly for values that largely deviate from the mean. Specifically, predictions for large ...
Abstract: Machine learning stands poised to revolutionize the process of scientific discovery across various disciplines. In this talk, we will introduce a state-of-the-art scientific machine learning ...
Objective: Approximately 10%-17% of patients with a recent small subcortical infarction (RSSI) experience an adverse functional prognosis at three months. Identifying risk factors for poor prognosis ...
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