Scaling Wearable Foundation Models

no code implementations • 17 Oct 2024 • Girish Narayanswamy, Xin Liu, Kumar Ayush, Yuzhe Yang, Xuhai Xu, Shun Liao, Jake Garrison, Shyam Tailor, Jake Sunshine, Yun Liu, Tim Althoff, Shrikanth Narayanan, Pushmeet Kohli, Jiening Zhan, Mark Malhotra, Shwetak Patel, Samy Abdel-Ghaffar, Daniel McDuff

Wearable sensors have become ubiquitous thanks to a variety of health tracking features.

Activity Recognition Heart Rate Variability +1

What Are the Odds? Language Models Are Capable of Probabilistic Reasoning

1 code implementation • 18 Jun 2024 • Akshay Paruchuri, Jake Garrison, Shun Liao, John Hernandez, Jacob Sunshine, Tim Althoff, Xin Liu, Daniel McDuff

Language models (LM) are capable of remarkably complex linguistic tasks; however, numerical reasoning is an area in which they frequently struggle.

HeAR -- Health Acoustic Representations

no code implementations • 4 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.

Deep Learning Self-Supervised Learning

Towards Accurate Differential Diagnosis with Large Language Models

no code implementations • 30 Nov 2023 • Daniel McDuff, Mike Schaekermann, Tao Tu, Anil Palepu, Amy Wang, Jake Garrison, Karan Singhal, Yash Sharma, Shekoofeh Azizi, Kavita Kulkarni, Le Hou, Yong Cheng, Yun Liu, S Sara Mahdavi, Sushant Prakash, Anupam Pathak, Christopher Semturs, Shwetak Patel, Dale R Webster, Ewa Dominowska, Juraj Gottweis, Joelle Barral, Katherine Chou, Greg S Corrado, Yossi Matias, Jake Sunshine, Alan Karthikesalingam, Vivek Natarajan

Comparing the two assisted study arms, the DDx quality score was higher for clinicians assisted by our LLM (top-10 accuracy 51. 7%) compared to clinicians without its assistance (36. 1%) (McNemar's Test: 45. 7, p < 0. 01) and clinicians with search (44. 4%) (4. 75, p = 0. 03).

Optimizing Audio Augmentations for Contrastive Learning of Health-Related Acoustic Signals

no code implementations • 11 Sep 2023 • Louis Blankemeier, Sebastien Baur, Wei-Hung Weng, Jake Garrison, Yossi Matias, Shruthi Prabhakara, Diego Ardila, Zaid Nabulsi

A crucial aspect of optimizing Slowfast NFNet for this application lies in identifying effective audio augmentations.

Contrastive Learning Medical Diagnosis +1