no code implementations • 6 Feb 2024 • Mert Ketenci, Iñigo Urteaga, Victor Alfonso Rodriguez, Noémie Elhadad, Adler Perotte
Shapley values have emerged as a foundational tool in machine learning (ML) for elucidating model decision-making processes.
no code implementations • 3 Nov 2023 • Mert Ketenci, Shreyas Bhave, Noémie Elhadad, Adler Perotte
We propose a survival analysis approach which eliminates the need to tune hyperparameters such as mixture assignments and bin sizes, reducing the burden on practitioners.
no code implementations • 2 Nov 2023 • Mert Ketenci, Adler Perotte, Noémie Elhadad, Iñigo Urteaga
We present a novel stochastic variational Gaussian process ($\mathcal{GP}$) inference method, based on a posterior over a learnable set of weighted pseudo input-output points (coresets).
no code implementations • 30 Apr 2022 • David Ouyang, John Theurer, Nathan R. Stein, J. Weston Hughes, Pierre Elias, Bryan He, Neal Yuan, Grant Duffy, Roopinder K. Sandhu, Joseph Ebinger, Patrick Botting, Melvin Jujjavarapu, Brian Claggett, James E. Tooley, Tim Poterucha, Jonathan H. Chen, Michael Nurok, Marco Perez, Adler Perotte, James Y. Zou, Nancy R. Cook, Sumeet S. Chugh, Susan Cheng, Christine M. Albert
The algorithm discriminates mortality with an AUC of 0. 83 (95% CI 0. 79-0. 87) surpassing the discrimination of the RCRI score with an AUC of 0. 67 (CI 0. 61-0. 72) in the held out test cohort.
no code implementations • 10 Nov 2021 • Chao Pang, Xinzhuo Jiang, Krishna S Kalluri, Matthew Spotnitz, Ruijun Chen, Adler Perotte, Karthik Natarajan
CEHR-BERT also demonstrated strong transfer learning capability, as our model trained on only 5% of data outperformed comparison models trained on the entire data set.
1 code implementation • 29 Sep 2020 • Griffin Adams, Mert Ketenci, Shreyas Bhave, Adler Perotte, Noémie Elhadad
We introduce Latent Meaning Cells, a deep latent variable model which learns contextualized representations of words by combining local lexical context and metadata.
no code implementations • 7 Dec 2018 • Victor Rodriguez, Adler Perotte
Disease phenotyping algorithms process observational clinical data to identify patients with specific diseases.
no code implementations • 21 May 2018 • Rajesh Ranganath, Adler Perotte
Together, these assumptions lead to a confounder estimator regularized by mutual information.
no code implementations • 6 Aug 2016 • Rajesh Ranganath, Adler Perotte, Noémie Elhadad, David Blei
The electronic health record (EHR) provides an unprecedented opportunity to build actionable tools to support physicians at the point of care.