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ipd: An R Package for Conducting Inference on Predicted Data

Autor: Salerno, Stephen, Miao, Jiacheng, Afiaz, Awan, Hoffman, Kentaro, Neufeld, Anna, Lu, Qiongshi, McCormick, Tyler H., Leek, Jeffrey T.

Summary: ipd is an open-source R software package for the downstream modeling of an outcome and its associated features where a potentially sizable portion of the outcome data has been imputed by an artificial intelligence or machine learning (AI/ML)

Externí odkaz: http://arxiv.org/abs/2410.09665

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Do We Really Even Need Data?

Autor: Hoffman, Kentaro, Salerno, Stephen, Afiaz, Awan, Leek, Jeffrey T., McCormick, Tyler H.

As artificial intelligence and machine learning tools become more accessible, and scientists face new obstacles to data collection (e.g. rising costs, declining survey response rates), researchers increasingly use predictions from pre-trained algorit

Externí odkaz: http://arxiv.org/abs/2401.08702

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Evaluation of software impact designed for biomedical research: Are we measuring what's meaningful?

Software is vital for the advancement of biology and medicine. Analysis of usage and impact metrics can help developers determine user and community engagement, justify additional funding, encourage additional use, identify unanticipated use cases, a

Externí odkaz: http://arxiv.org/abs/2306.03255

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