Predicting sample size required for classification performance
2012 · BMC Medical Informatics and Decision Making · 508 citations
https://doi.org/10.1186/1472-6947-12-8
Q
Qing Zeng‐Treitler
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Qing Zeng‐Treitler is an academic researcher from United States Department of Veterans Affairs. The author has contributed to research in topics: Biomedical Text Mining and Ontologies & Machine Learning in Healthcare & Topic Modeling. The author has an h-index of 29, co-authored 131 publications.
ORCID: 0000-0002-8353-7473Papers by this author
worksActive learning for clinical text classification: is it better than random sampling?
2012 · Journal of the American Medical Informatics Association · 80 citations
https://doi.org/10.1136/amiajnl-2011-000648
An efficient prototype method to identify and correct misspellings in clinical text
2019 · BMC Research Notes · 16 citations
https://doi.org/10.1186/s13104-019-4073-y
ChatGPT-4 extraction of heart failure symptoms and signs from electronic health records
2024 · Progress in Cardiovascular Diseases · 9 citations
https://doi.org/10.1016/j.pcad.2024.10.010
Outcomes of KDIGO-Defined CKD in U.S. Veterans With HFpEF, HFmrEF, and HFrEF
2025 · JACC Heart Failure · 9 citations
https://doi.org/10.1016/j.jchf.2024.11.007
The effect of simulated narratives that leverage EMR data on shared decision-making: a pilot study
2016 · BMC Research Notes · 7 citations
https://doi.org/10.1186/s13104-016-2152-x
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