Eric K. Oermann

According to our database, Eric K. Oermann authored at least 28 papers between 2017 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Clinically Grounded Agent-based Report Evaluation: An Interpretable Metric for Radiology Report Generation.

Radhika Dua, Young Joon Fred Kwon, Siddhant Dogra, Daniel Freedman, Diana Ruan, Motaz Nashawaty, Danielle Rigau, Daniel Alexander Alber, Kang Zhang, Kyunghyun Cho, Eric Karl Oermann

CoRR, August, 2025

Evaluating the performance and fragility of large language models on the self-assessment for neurological surgeons.

Krithik Vishwanath, Anton Alyakin, Mrigayu Ghosh, Jin Vivian Lee, Daniel Alexander Alber, Karl L. Sangwon, Douglas Kondziolka, Eric Karl Oermann

CoRR, May, 2025

Gateformer: Advancing Multivariate Time Series Forecasting through Temporal and Variate-Wise Attention with Gated Representations.

Yu-Hsiang Lan, Eric K. Oermann

CoRR, May, 2025

Medical large language models are easily distracted.

Krithik Vishwanath, Anton Alyakin, Daniel Alexander Alber, Jin Vivian Lee, Douglas Kondziolka, Eric Karl Oermann

CoRR, April, 2025

It is Too Many Options: Pitfalls of Multiple-Choice Questions in Generative AI and Medical Education.

Shrutika Singh, Anton Alyakin, Daniel Alexander Alber, Jaden Stryker, Ai Phuong S. Tong, Karl L. Sangwon, Nicolas K. Goff, Mathew de la Paz, Miguel Hernandez-Rovira, Ki Yun Park, Eric Claude Leuthardt, Eric Karl Oermann

CoRR, March, 2025

BPQA Dataset: Evaluating How Well Language Models Leverage Blood Pressures to Answer Biomedical Questions.

Chi Hang, Ruiqi Deng, Lavender Yao Jiang, Zihao Yang, Anton Alyakin, Daniel Alber, Eric Karl Oermann

CoRR, March, 2025

Repurposing the scientific literature with vision-language models.

Anton Alyakin, Jaden Stryker, Daniel Alexander Alber, Karl L. Sangwon, Brandon Duderstadt, Akshay Save, David Kurland, Spencer Frome, Shrutika Singh, Jeff Zhang, Eunice Yang, Ki Yun Park, Cordelia Orillac, Aly A. Valliani, Sean Neifert, Albert Liu, Aneek Patel, Christopher Livia, Darryl Lau, Ilya Laufer, Peter A. Rozman, Eveline Teresa Hidalgo, Howard Riina, Rui Feng, Todd C. Hollon, Yindalon Aphinyanaphongs, John G. Golfinos, Laura Snyder, Eric Leuthardt, Douglas Kondziolka, Eric Karl Oermann

CoRR, February, 2025

HeadCT-ONE: Enabling Granular and Controllable Automated Evaluation of Head CT Radiology Report Generation.

Julián Nicolás Acosta, Xiaoman Zhang, Siddhant Dogra, Hong-Yu Zhou, Seyedmehdi Payabvash, Guido J. Falcone, Eric Karl Oermann, Pranav Rajpurkar

Proceedings of the Conference on Health, 2025

2024

Concepts and applications of digital twins in healthcare and medicine.

Kang Zhang, Hong-Yu Zhou, Daniel T. Baptista-Hon, Yuanxu Gao, Xiaohong Liu, Eric K. Oermann, Sheng Xu, Shengwei Jin, Jian Zhang, Zhuo Sun, Yun Yin, Ronald M. Razmi, Alexandre Loupy, Stephan Beck, Jia Qu, Joseph Wu

Patterns, 2024

MedG-KRP: Medical Graph Knowledge Representation Probing.

Gabriel R. Rosenbaum, Lavender Yao Jiang, Ivaxi Sheth, Jaden Stryker, Anton Alyakin, Daniel Alexander Alber, Nicolas K. Goff, Young Joon Fred Kwon, John Markert, Mustafa Nasir-Moin, Jan Moritz Niehues, Karl L. Sangwon, Eunice Yang, Eric Karl Oermann

CoRR, 2024

MedMobile: A mobile-sized language model with expert-level clinical capabilities.

Krithik Vishwanath, Jaden Stryker, Anton Alyakin, Daniel Alexander Alber, Eric Karl Oermann

CoRR, 2024

Refining Packing and Shuffling Strategies for Enhanced Performance in Generative Language Models.

Yanbing Chen, Ruilin Wang, Zihao Yang, Lavender Yao Jiang, Eric Karl Oermann

CoRR, 2024

Generalization in Healthcare AI: Evaluation of a Clinical Large Language Model.

Salman Rahman, Lavender Yao Jiang, Saadia Gabriel, Yindalon Aphinyanaphongs, Eric Karl Oermann, Rumi Chunara

CoRR, 2024

2023

Health system-scale language models are all-purpose prediction engines.

Lavender Yao Jiang, Xujin Chris Liu, Nima Pour Nejatian, Mustafa Nasir-Moin, Duo Wang, Anas Z. Abidin, Kevin Eaton, Howard Antony Riina, Ilya Laufer, Paawan Punjabi, Madeline Miceli, Nora C. Kim, Cordelia Orillac, Zane Schnurman, Christopher Livia, Hannah Weiss, David Kurland, Sean Neifert, Yosef Dastagirzada, Douglas Kondziolka, Alexander T. M. Cheung, Grace Yang, Ming Cao, Mona Flores, Anthony B. Costa, Yindalon Aphinyanaphongs, Kyunghyun Cho, Eric Karl Oermann

Nat., 2023

Making the Most Out of the Limited Context Length: Predictive Power Varies with Clinical Note Type and Note Section.

Hongyi Zheng, Yixin Zhu, Lavender Y. Jiang, Kyunghyun Cho, Eric K. Oermann

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, 2023

Intriguing Effect of the Correlation Prior on ICD-9 Code Assignment.

Zihao Yang, Chenkang Zhang, Muru Wu, Xujin Liu, Lavender Y. Jiang, Kyunghyun Cho, Eric K. Oermann

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, 2023

2022

Autoencoders for sample size estimation for fully connected neural network classifiers.

Faris F. Gulamali, Ashwin Sawant, Patricia H. Kovatch, Benjamin S. Glicksberg, Alexander Charney, Girish N. Nadkarni, Eric K. Oermann

npj Digit. Medicine, 2022

Language Model Classifier Aligns Better with Physician Word Sensitivity than XGBoost on Readmission Prediction.

Grace Yang, Ming Cao, Lavender Y. Jiang, Xujin Chris Liu, Alexander T. M. Cheung, Hannah Weiss, David Kurland, Kyunghyun Cho, Eric K. Oermann

CoRR, 2022

Out-of-distribution Generalization in the Presence of Nuisance-Induced Spurious Correlations.

Aahlad Manas Puli, Lily H. Zhang, Eric Karl Oermann, Rajesh Ranganath

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Identifying and mitigating bias in algorithms used to manage patients in a pandemic.

Yifan Li, Garrett Yoon, Mustafa Nasir-Moin, David Rosenberg, Sean Neifert, Douglas Kondziolka, Eric Karl Oermann

CoRR, 2021

Patient level simulation and reinforcement learning to discover novel strategies for treating ovarian cancer.

Brian Murphy, Mustafa Nasir-Moin, Grace von Oiste, Viola Chen, Howard Antony Riina, Douglas Kondziolka, Eric K. Oermann

CoRR, 2021

Stereo Video Reconstruction Without Explicit Depth Maps for Endoscopic Surgery.

Annika Brundyn, Jesse Swanson, Kyunghyun Cho, Doug Kondziolka, Eric K. Oermann

CoRR, 2021

Predictive Modeling in the Presence of Nuisance-Induced Spurious Correlations.

Aahlad Manas Puli, Lily H. Zhang, Eric K. Oermann, Rajesh Ranganath

CoRR, 2021

2020

The Utility of General Domain Transfer Learning for Medical Language Tasks.

Daniel Ranti, Katie Hanss, Shan Zhao, Varun Arvind, Joseph J. Titano, Anthony B. Costa, Eric K. Oermann

CoRR, 2020

Appropriate Evaluation of Diagnostic Utility of Machine Learning Algorithm Generated Images.

Young Joon Kwon, Danielle Toussie, Lea Azour, Jose Concepcion, Corey Eber, G. Anthony Reina, Ping Tak Peter Tang, Amish H. Doshi, Eric K. Oermann, Anthony B. Costa

Proceedings of the Machine Learning for Health Workshop, 2020

2019

CANDI: an R package and Shiny app for annotating radiographs and evaluating computer-aided diagnosis.

Marcus A. Badgeley, Manway Liu, Benjamin S. Glicksberg, Mark M. Shervey, John R. Zech, Khader Shameer, Joseph Lehar, Eric K. Oermann, Michael V. McConnell, Thomas M. Snyder, Joel T. Dudley

Bioinform., 2019

2018

Confounding variables can degrade generalization performance of radiological deep learning models.

John R. Zech, Marcus A. Badgeley, Manway Liu, Anthony B. Costa, Joseph J. Titano, Eric K. Oermann

CoRR, 2018

2017

Wide and deep volumetric residual networks for volumetric image classification.

Varun Arvind, Anthony B. Costa, Marcus A. Badgeley, Samuel K. Cho, Eric K. Oermann

CoRR, 2017