What are the Desired Characteristics of Calibration Sets? Identifying Correlates on Long Form Scientific Summarization

1 code implementation • 12 May 2023 • Griffin Adams, Bichlien H Nguyen, Jake Smith, Yingce Xia, Shufang Xie, Anna Ostropolets, Budhaditya Deb, Yuan-Jyue Chen, Tristan Naumann, Noémie Elhadad

Summarization models often generate text that is poorly calibrated to quality metrics because they are trained to maximize the likelihood of a single reference (MLE).

Causal Fairness Assessment of Treatment Allocation with Electronic Health Records

no code implementations • 21 Nov 2022 • Linying Zhang, Lauren R. Richter, Yixin Wang, Anna Ostropolets, Noemie Elhadad, David M. Blei, George Hripcsak

Healthcare continues to grapple with the persistent issue of treatment disparities, sparking concerns regarding the equitable allocation of treatments in clinical practice.

Causal Inference Decision Making +1

Ontologizing Health Systems Data at Scale: Making Translational Discovery a Reality

1 code implementation • 10 Sep 2022 • Tiffany J. Callahan, Adrianne L. Stefanski, Jordan M. Wyrwa, Chenjie Zeng, Anna Ostropolets, Juan M. Banda, William A. Baumgartner Jr., Richard D. Boyce, Elena Casiraghi, Ben D. Coleman, Janine H. Collins, Sara J. Deakyne-Davies, James A. Feinstein, Melissa A. Haendel, Asiyah Y. Lin, Blake Martin, Nicolas A. Matentzoglu, Daniella Meeker, Justin Reese, Jessica Sinclair, Sanya B. Taneja, Katy E. Trinkley, Nicole A. Vasilevsky, Andrew Williams, Xingman A. Zhang, Joshua C. Denny, Peter N. Robinson, Patrick Ryan, George Hripcsak, Tellen D. Bennett, Lawrence E. Hunter, Michael G. Kahn

Background: Common data models solve many challenges of standardizing electronic health record (EHR) data, but are unable to semantically integrate all the resources needed for deep phenotyping.

The Medical Deconfounder: Assessing Treatment Effects with Electronic Health Records

no code implementations • 3 Apr 2019 • Linying Zhang, Yixin Wang, Anna Ostropolets, Jami J. Mulgrave, David M. Blei, George Hripcsak

To adjust for unobserved confounders, we develop the medical deconfounder, a machine learning algorithm that unbiasedly estimates treatment effects from EHRs.