Summary of Towards a Personal Health Large Language Model, by Justin Cosentino et al.

Towards a Personal Health Large Language Model

by Justin Cosentino, Anastasiya Belyaeva, Xin Liu, Nicholas A. Furlotte, Zhun Yang, Chace Lee, Erik Schenck, Yojan Patel, Jian Cui, Logan Douglas Schneider, Robby Bryant, Ryan G. Gomes, Allen Jiang, Roy Lee, Yun Liu, Javier Perez, Jameson K. Rogers, Cathy Speed, Shyam Tailor, Megan Walker, Jeffrey Yu, Tim Althoff, Conor Heneghan, John Hernandez, Mark Malhotra, Leor Stern, Yossi Matias, Greg S. Corrado, Shwetak Patel, Shravya Shetty, Jiening Zhan, Shruthi Prabhakara, Daniel McDuff, Cory Y. McLean

First submitted to arxiv on: 10 Jun 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary This paper presents Personal Health Large Language Model (PH-LLM), a model fine-tuned from Gemini to understand and reason over numerical time-series personal health data. Unlike most large language models, which focus on clinical tasks, PH-LLM is designed for mobile and wearable devices that provide rich, longitudinal data for personal health monitoring. The authors created three datasets to test PH-LLM’s capabilities: production of personalized insights and recommendations from sleep patterns, physical activity, and physiological responses; expert domain knowledge; and prediction of self-reported sleep outcomes. Comprehensive evaluation shows that PH-LLM performs similarly to expert performance in fitness and provides significant improvements in using relevant domain knowledge for sleep insights. Additionally, PH-LLM demonstrates broad knowledge and capabilities in predicting self-reported sleep quality outcomes from wearable data. These results highlight the potential benefits of contextualizing physiological data for personal health applications.
Low	GrooveSquid.com (original content)	Low Difficulty Summary This research paper is about creating a new computer program that can understand and make sense of personal health information collected from mobile devices and wearables. Most similar programs focus on helping doctors, but this one is designed to help people monitor their own health over time. The authors tested the program with three different sets of data: producing personalized recommendations for sleep and physical activity, showing domain expertise in sleep medicine and fitness, and predicting self-reported sleep quality. The results show that the program can provide valuable insights and recommendations for personal health monitoring.