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8 results on '"Finlayson, Sam"'

1. ML4H Abstract Track 2019

Author

McDermott, Matthew B. A., Alsentzer, Emily, Finlayson, Sam, Oberst, Michael, Falck, Fabian, Naumann, Tristan, Beaulieu-Jones, Brett K., and Dalca, Adrian V.

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

A collection of the accepted abstracts for the Machine Learning for Health (ML4H) workshop at NeurIPS 2019. This index is not complete, as some accepted abstracts chose to opt-out of inclusion.

Published
2020

2. Machine Learning for Health (ML4H) Workshop at NeurIPS 2018

Author

Antropova, Natalia, Beam, Andrew L., Beaulieu-Jones, Brett K., Chen, Irene, Chivers, Corey, Dalca, Adrian, Finlayson, Sam, Fiterau, Madalina, Fries, Jason Alan, Ghassemi, Marzyeh, Hughes, Mike, Jedynak, Bruno, Kandola, Jasvinder S., McDermott, Matthew, Naumann, Tristan, Schulam, Peter, Shamout, Farah, and Yahi, Alexandre

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

This volume represents the accepted submissions from the Machine Learning for Health (ML4H) workshop at the conference on Neural Information Processing Systems (NeurIPS) 2018, held on December 8, 2018 in Montreal, Canada., Comment: Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 arXiv:1811.07216

Published
2018

3. Text Mining for Adverse Drug Events: the Promise, Challenges, and State of the Art

Author

Harpaz, Rave, Callahan, Alison, Tamang, Suzanne, Low, Yen, Odgers, David, Finlayson, Sam, Jung, Kenneth, LePendu, Paea, and Shah, Nigam H

Subjects
Patient Safety, Data Collection, Data Mining, Databases, Factual, Drug Labeling, Humans, Internet, Periodicals as Topic, Pharmacovigilance, Social Media, Pharmacology and Pharmaceutical Sciences, Pharmacology & Pharmacy
Abstract

Text mining is the computational process of extracting meaningful information from large amounts of unstructured text. It is emerging as a tool to leverage underutilized data sources that can improve pharmacovigilance, including the objective of adverse drug event (ADE) detection and assessment. This article provides an overview of recent advances in pharmacovigilance driven by the application of text mining, and discusses several data sources-such as biomedical literature, clinical narratives, product labeling, social media, and Web search logs-that are amenable to text mining for pharmacovigilance. Given the state of the art, it appears text mining can be applied to extract useful ADE-related information from multiple textual sources. Nonetheless, further research is required to address remaining technical challenges associated with the text mining methodologies, and to conclusively determine the relative contribution of each textual source to improving pharmacovigilance.

Published
2014

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