Search Results for author: Christina Chen

Found 14 papers, 1 papers with code

HeAR -- Health Acoustic Representations

no code implementations4 Mar 2024 Sebastien Baur, Zaid Nabulsi, Wei-Hung Weng, Jake Garrison, Louis Blankemeier, Sam Fishman, Christina Chen, Sujay Kakarmath, Minyoi Maimbolwa, Nsala Sanjase, Brian Shuma, Yossi Matias, Greg S. Corrado, Shwetak Patel, Shravya Shetty, Shruthi Prabhakara, Monde Muyoyeta, Diego Ardila

Health acoustic sounds such as coughs and breaths are known to contain useful health signals with significant potential for monitoring health and disease, yet are underexplored in the medical machine learning community.

Self-Supervised Learning

Predicting Cardiovascular Disease Risk using Photoplethysmography and Deep Learning

no code implementations9 May 2023 Wei-Hung Weng, Sebastien Baur, Mayank Daswani, Christina Chen, Lauren Harrell, Sujay Kakarmath, Mariam Jabara, Babak Behsaz, Cory Y. McLean, Yossi Matias, Greg S. Corrado, Shravya Shetty, Shruthi Prabhakara, Yun Liu, Goodarz Danaei, Diego Ardila

We compared the DLS with the office-based refit-WHO score, which adopts the shared predictors from WHO and Globorisk scores (age, sex, smoking status, height, weight and systolic blood pressure) but refitted on the UK Biobank (UKB) cohort.

Photoplethysmography (PPG)

Machine learning for dynamically predicting the onset of renal replacement therapy in chronic kidney disease patients using claims data

no code implementations3 Sep 2022 Daniel Lopez-Martinez, Christina Chen, Ming-Jun Chen

In this work, we present a machine learning model that dynamically identifies CKD patients at risk of requiring RRT up to one year in advance using only claims data.

Specificity

Simplified Transfer Learning for Chest Radiography Models Using Less Data

1 code implementation Radiology 2022 Andrew B. Sellergren, Christina Chen, Zaid Nabulsi, Yuanzhen Li, Aaron Maschinot, Aaron Sarna, Jenny Huang, Charles Lau, Sreenivasa Raju Kalidindi, Mozziyar Etemadi, Florencia Garcia-Vicente, David Melnick, Yun Liu, Krish Eswaran, Daniel Tse, Neeral Beladia, Dilip Krishnan, Shravya Shetty

Supervised contrastive learning enabled performance comparable to state-of-the-art deep learning models in multiple clinical tasks by using as few as 45 images and is a promising method for predictive modeling with use of small data sets and for predicting outcomes in shifting patient populations.

Contrastive Learning Transfer Learning

Boosting the interpretability of clinical risk scores with intervention predictions

no code implementations6 Jul 2022 Eric Loreaux, Ke Yu, Jonas Kemp, Martin Seneviratne, Christina Chen, Subhrajit Roy, Ivan Protsyuk, Natalie Harris, Alexander D'Amour, Steve Yadlowsky, Ming-Jun Chen

We propose a joint model of intervention policy and adverse event risk as a means to explicitly communicate the model's assumptions about future interventions.

Disability prediction in multiple sclerosis using performance outcome measures and demographic data

no code implementations8 Apr 2022 Subhrajit Roy, Diana Mincu, Lev Proleev, Negar Rostamzadeh, Chintan Ghate, Natalie Harris, Christina Chen, Jessica Schrouff, Nenad Tomasev, Fletcher Lee Hartsell, Katherine Heller

To the best of our knowledge, our results are the first to show that it is possible to predict disease progression using POMs and demographic data in the context of both clinical trials and smartphone-base studies by using two datasets.

Benchmarking BIG-bench Machine Learning

AI system for fetal ultrasound in low-resource settings

no code implementations18 Mar 2022 Ryan G. Gomes, Bellington Vwalika, Chace Lee, Angelica Willis, Marcin Sieniek, Joan T. Price, Christina Chen, Margaret P. Kasaro, James A. Taylor, Elizabeth M. Stringer, Scott Mayer McKinney, Ntazana Sindano, George E. Dahl, William Goodnight III, Justin Gilmer, Benjamin H. Chi, Charles Lau, Terry Spitz, T Saensuksopa, Kris Liu, Jonny Wong, Rory Pilgrim, Akib Uddin, Greg Corrado, Lily Peng, Katherine Chou, Daniel Tse, Jeffrey S. A. Stringer, Shravya Shetty

Using a simplified sweep protocol with real-time AI feedback on sweep quality, we have demonstrated the generalization of model performance to minimally trained novice ultrasound operators using low cost ultrasound devices with on-device AI integration.

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