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What's in a Summary? Laying the Groundwork for Advances in Hospital-Course Summarization


Apr 12, 2021
Griffin Adams, Emily Alsentzer, Mert Ketenci, Jason Zucker, Noémie Elhadad

* NAACL 2021 

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A generative, predictive model for menstrual cycle lengths that accounts for potential self-tracking artifacts in mobile health data


Mar 16, 2021
Kathy Li, Iñigo Urteaga, Amanda Shea, Virginia J. Vitzthum, Chris H. Wiggins, Noémie Elhadad

* Extended version of the work presented at the NeurIPS 2020 Machine Learning for Mobile Health Workshop (see https://sites.google.com/view/ml4mobilehealth-neurips-2020/home

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Exploring Gender Disparities in Time to Diagnosis


Nov 15, 2020
Tony Y. Sun, Oliver J. Bear Don't Walk IV, Jennifer L. Chen, Harry Reyes Nieva, Noémie Elhadad

* Machine Learning for Health (ML4H) at NeurIPS 2020 - Extended Abstract 

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Towards Patient Record Summarization Through Joint Phenotype Learning in HIV Patients


Mar 09, 2020
Gal Levy-Fix, Jason Zucker, Konstantin Stojanovic, Noémie Elhadad


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Multi-Task Gaussian Processes and Dilated Convolutional Networks for Reconstruction of Reproductive Hormonal Dynamics


Aug 27, 2019
Iñigo Urteaga, Tristan Bertin, Theresa M. Hardy, David J. Albers, Noémie Elhadad

* Accepted and presented in Machine Learning for Healthcare 2019 

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Machine Learning and Visualization in Clinical Decision Support: Current State and Future Directions


Jun 06, 2019
Gal Levy-Fix, Gilad J. Kuperman, Noémie Elhadad


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Phenotyping Endometriosis through Mixed Membership Models of Self-Tracking Data


Nov 06, 2018
Iñigo Urteaga, Mollie McKillop, Sharon Lipsky-Gorman, Noémie Elhadad

* As presented in Machine Learning for Healthcare 2018, https://www.mlforhc.org/2018-conference/ 

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Generative Adversarial Networks for Electronic Health Records: A Framework for Exploring and Evaluating Methods for Predicting Drug-Induced Laboratory Test Trajectories


Dec 01, 2017
Alexandre Yahi, Rami Vanguri, Noémie Elhadad, Nicholas P. Tatonetti

* NIPS ML4H 2017 

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Towards Personalized Modeling of the Female Hormonal Cycle: Experiments with Mechanistic Models and Gaussian Processes


Nov 30, 2017
Iñigo Urteaga, David J. Albers, Marija Vlajic Wheeler, Anna Druet, Hans Raffauf, Noémie Elhadad

* Accepted at NIPS 2017 Workshop on Machine Learning for Health (https://ml4health.github.io/2017/

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Deep Survival Analysis


Sep 18, 2016
Rajesh Ranganath, Adler Perotte, Noémie Elhadad, David Blei

* Presented at 2016 Machine Learning and Healthcare Conference (MLHC 2016), Los Angeles, CA 

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