Kaiser Permanente Washington Health Research Institute

Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms

Thomas Schaffter, Diana S.M. Buist, Christoph I. Lee et al.

2020 · 409 Zit.

Tradeoffs between accuracy measures for electronic health care data algorithms

Jessica Chubak, Gaia Pocobelli, Noel S. Weiss

2011 · 214 Zit.

Challenges in adapting existing clinical natural language processing systems to multiple, diverse health care settings

David Carrell, Robert E. Schoen, Daniel A. Leffler et al.

2017 · 178 Zit.

Will Machine Learning Tip the Balance in Breast Cancer Screening?

Andrew D. Trister, Diana S.M. Buist, Christoph I. Lee

2017 · 90 Zit.

Comparison of Natural Language Processing Rules-based and Machine-learning Systems to Identify Lumbar Spine Imaging Findings Related to Low Back Pain

Wang-Chiew Tan, Saeed Hassanpour, Patrick J. Heagerty et al.

2018 · 86 Zit.

Population-Based Analysis of Histologically Confirmed Melanocytic Proliferations Using Natural Language Processing

Jason P. Lott, Denise M. Boudreau, Ray L. Barnhill et al.

2017 · 71 Zit.

Natural Language Processing to identify pneumonia from radiology reports

Sascha Dublin, Eric Baldwin, Rod Walker et al.

2013 · 66 Zit.

Independent External Validation of Artificial Intelligence Algorithms for Automated Interpretation of Screening Mammography: A Systematic Review

A Anderson, M. Luke Marinovich, Nehmat Houssami et al.

2022 · 58 Zit.

External Validation of an Ensemble Model for Automated Mammography Interpretation by Artificial Intelligence

William Hsu, Daniel S. Hippe, Noor Nakhaei et al.

2022 · 51 Zit.

Using Natural Language Processing of Free-Text Radiology Reports to Identify Type 1 Modic Endplate Changes

Hannu Huhdanpaa, Wang-Chiew Tan, Sean D. Rundell et al.

2017 · 44 Zit.

Validation of natural language processing to extract breast cancer pathology procedures and results

Arika E. Wieneke, Erin J. Aiello Bowles, David Cronkite et al.

2015 · 40 Zit.

Making work visible for electronic phenotype implementation: Lessons learned from the eMERGE network

Ning Shang, Cong Liu, Luke V. Rasmussen et al.

2019 · 32 Zit.

Artificial Intelligence Algorithm for Subclinical Breast Cancer Detection

Jonas Gjesvik, Nataliia Moshina, Christoph I. Lee et al.

2024 · 26 Zit.

Evaluation of the portability of computable phenotypes with natural language processing in the eMERGE network

Jennifer A. Pacheco, Luke V. Rasmussen, Ken Wiley et al.

2023 · 22 Zit.

Pathways to breast cancer screening artificial intelligence algorithm validation

Christoph I. Lee, Nehmat Houssami, Joann G. Elmore et al.

2019 · 20 Zit.