Search Results for author: Noémie Elhadad

Found 13 papers, 5 papers with code

Learning to Revise References for Faithful Summarization

1 code implementation13 Apr 2022 Griffin Adams, Han-Chin Shing, Qing Sun, Christopher Winestock, Kathleen McKeown, Noémie Elhadad

In real-world scenarios with naturally occurring datasets, reference summaries are noisy and may contain information that cannot be inferred from the source text.

Clinical Knowledge Contrastive Learning +1

Assessing Phenotype Definitions for Algorithmic Fairness

no code implementations10 Mar 2022 Tony Y. Sun, Shreyas Bhave, Jaan Altosaar, Noémie Elhadad

While there are multiple potential sources of bias when constructing phenotype definitions which may affect their fairness, it is not standard in the field of phenotyping to consider the impact of different definitions across subgroups of patients.


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

1 code implementation24 Feb 2021 Kathy Li, Iñigo Urteaga, Amanda Shea, Virginia J. Vitzthum, Chris H. Wiggins, Noémie Elhadad

Our model offers several advantages: 1) accounting explicitly for self-tracking artifacts yields better prediction accuracy as likelihood of skipping increases; 2) because it is a generative model, predictions can be updated online as a given cycle evolves, and we can gain interpretable insight into how these predictions change over time; and 3) its hierarchical nature enables modeling of an individual's cycle length history while incorporating population-level information.

Zero-Shot Clinical Acronym Expansion via Latent Meaning Cells

1 code implementation29 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.

Representation Learning

Towards Patient Record Summarization Through Joint Phenotype Learning in HIV Patients

no code implementations9 Mar 2020 Gal Levy-Fix, Jason Zucker, Konstantin Stojanovic, Noémie Elhadad

In this paper, we focus our experiments on assessing the learned phenotypes and their relatedness as learned from a specific patient population.

Variational Inference

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

1 code implementation27 Aug 2019 Iñigo Urteaga, Tristan Bertin, Theresa M. Hardy, David J. Albers, Noémie Elhadad

We present an end-to-end statistical framework for personalized, accurate, and minimally invasive modeling of female reproductive hormonal patterns.

Gaussian Processes

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

no code implementations6 Jun 2019 Gal Levy-Fix, Gilad J. Kuperman, Noémie Elhadad

Heretofore, there has not been a review of ways in which research in machine learning and other types of data-driven techniques can contribute effectively to clinical care and the types of support they can bring to clinicians.

BIG-bench Machine Learning Decision Making

Phenotyping Endometriosis through Mixed Membership Models of Self-Tracking Data

no code implementations6 Nov 2018 Iñigo Urteaga, Mollie McKillop, Sharon Lipsky-Gorman, Noémie Elhadad

We investigate the use of self-tracking data and unsupervised mixed-membership models to phenotype endometriosis.

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

1 code implementation30 Nov 2017 Iñigo Urteaga, David J. Albers, Marija Vlajic Wheeler, Anna Druet, Hans Raffauf, Noémie Elhadad

The motivation for this work is to model the hormonal cycle and predict its phases in time, both for healthy individuals and for those with disorders of the reproductive system.

Gaussian Processes

Deep Survival Analysis

no code implementations6 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.

Survival Analysis

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