title: Towards a Personal Health Large Language Model
summary: In health, most large language model (LLM) research has focused on clinical
  tasks. However, mobile and wearable devices, which are rarely integrated into
  such tasks, provide rich, longitudinal data for personal health monitoring.
  Here we present Personal Health Large Language Model (PH-LLM), fine-tuned from
  Gemini for understanding and reasoning over numerical time-series personal
  health data. We created and curated three datasets that test 1) production of
  personalized insights and recommendations from sleep patterns, physical
  activity, and physiological responses, 2) expert domain knowledge, and 3)
  prediction of self-reported sleep outcomes. For the first task we designed 857
  case studies in collaboration with domain experts to assess real-world
  scenarios in sleep and fitness. Through comprehensive evaluation of
  domain-specific rubrics, we observed that Gemini Ultra 1.0 and PH-LLM are not
  statistically different from expert performance in fitness and, while experts
  remain superior for sleep, fine-tuning PH-LLM provided significant improvements
  in using relevant domain knowledge and personalizing information for sleep
  insights. We evaluated PH-LLM domain knowledge using multiple choice sleep
  medicine and fitness examinations. PH-LLM achieved 79% on sleep and 88% on
  fitness, exceeding average scores from a sample of human experts. Finally, we
  trained PH-LLM to predict self-reported sleep quality outcomes from textual and
  multimodal encoding representations of wearable data, and demonstrate that
  multimodal encoding is required to match performance of specialized
  discriminative models. Although further development and evaluation are
  necessary in the safety-critical personal health domain, these results
  demonstrate both the broad knowledge and capabilities of Gemini models and the
  benefit of contextualizing physiological data for personal health applications
  as done with PH-LLM.
paper_authors._id: 6667c6690c21d689e19101e7
paper_authors.name: Justin Cosentino
paper_authors._id: 6667c6690c21d689e19101e8
paper_authors.name: Anastasiya Belyaeva
paper_authors._id: 6667c6690c21d689e19101e9
paper_authors.name: Xin Liu
paper_authors._id: 6667c6690c21d689e19101ea
paper_authors.name: Nicholas A. Furlotte
paper_authors._id: 6667c6690c21d689e19101eb
paper_authors.name: Zhun Yang
paper_authors._id: 6667c6690c21d689e19101ec
paper_authors.name: Chace Lee
paper_authors._id: 6667c6690c21d689e19101ed
paper_authors.name: Erik Schenck
paper_authors._id: 6667c6690c21d689e19101ee
paper_authors.name: Yojan Patel
paper_authors._id: 6667c6690c21d689e19101ef
paper_authors.name: Jian Cui
paper_authors._id: 6667c6690c21d689e19101f0
paper_authors.name: Logan Douglas Schneider
paper_authors._id: 6667c6690c21d689e19101f1
paper_authors.name: Robby Bryant
paper_authors._id: 6667c6690c21d689e19101f2
paper_authors.name: Ryan G. Gomes
paper_authors._id: 6667c6690c21d689e19101f3
paper_authors.name: Allen Jiang
paper_authors._id: 6667c6690c21d689e19101f4
paper_authors.name: Roy Lee
paper_authors._id: 6667c6690c21d689e19101f5
paper_authors.name: Yun Liu
paper_authors._id: 6667c6690c21d689e19101f6
paper_authors.name: Javier Perez
paper_authors._id: 6667c6690c21d689e19101f7
paper_authors.name: Jameson K. Rogers
paper_authors._id: 6667c6690c21d689e19101f8
paper_authors.name: Cathy Speed
paper_authors._id: 6667c6690c21d689e19101f9
paper_authors.name: Shyam Tailor
paper_authors._id: 6667c6690c21d689e19101fa
paper_authors.name: Megan Walker
paper_authors._id: 6667c6690c21d689e19101fb
paper_authors.name: Jeffrey Yu
paper_authors._id: 6667c6690c21d689e19101fc
paper_authors.name: Tim Althoff
paper_authors._id: 6667c6690c21d689e19101fd
paper_authors.name: Conor Heneghan
paper_authors._id: 6667c6690c21d689e19101fe
paper_authors.name: John Hernandez
paper_authors._id: 6667c6690c21d689e19101ff
paper_authors.name: Mark Malhotra
paper_authors._id: 6667c6690c21d689e1910200
paper_authors.name: Leor Stern
paper_authors._id: 6667c6690c21d689e1910201
paper_authors.name: Yossi Matias
paper_authors._id: 6667c6690c21d689e1910202
paper_authors.name: Greg S. Corrado
paper_authors._id: 6667c6690c21d689e1910203
paper_authors.name: Shwetak Patel
paper_authors._id: 6667c6690c21d689e1910204
paper_authors.name: Shravya Shetty
paper_authors._id: 6667c6690c21d689e1910205
paper_authors.name: Jiening Zhan
paper_authors._id: 6667c6690c21d689e1910206
paper_authors.name: Shruthi Prabhakara
paper_authors._id: 6667c6690c21d689e1910207
paper_authors.name: Daniel McDuff
paper_authors._id: 6667c6690c21d689e1910208
paper_authors.name: Cory Y. McLean
paper_title: Towards a Personal Health Large Language Model
paper_summary: In health, most large language model (LLM) research has focused on clinical
  tasks. However, mobile and wearable devices, which are rarely integrated into
  such tasks, provide rich, longitudinal data for personal health monitoring.
  Here we present Personal Health Large Language Model (PH-LLM), fine-tuned from
  Gemini for understanding and reasoning over numerical time-series personal
  health data. We created and curated three datasets that test 1) production of
  personalized insights and recommendations from sleep patterns, physical
  activity, and physiological responses, 2) expert domain knowledge, and 3)
  prediction of self-reported sleep outcomes. For the first task we designed 857
  case studies in collaboration with domain experts to assess real-world
  scenarios in sleep and fitness. Through comprehensive evaluation of
  domain-specific rubrics, we observed that Gemini Ultra 1.0 and PH-LLM are not
  statistically different from expert performance in fitness and, while experts
  remain superior for sleep, fine-tuning PH-LLM provided significant improvements
  in using relevant domain knowledge and personalizing information for sleep
  insights. We evaluated PH-LLM domain knowledge using multiple choice sleep
  medicine and fitness examinations. PH-LLM achieved 79% on sleep and 88% on
  fitness, exceeding average scores from a sample of human experts. Finally, we
  trained PH-LLM to predict self-reported sleep quality outcomes from textual and
  multimodal encoding representations of wearable data, and demonstrate that
  multimodal encoding is required to match performance of specialized
  discriminative models. Although further development and evaluation are
  necessary in the safety-critical personal health domain, these results
  demonstrate both the broad knowledge and capabilities of Gemini models and the
  benefit of contextualizing physiological data for personal health applications
  as done with PH-LLM.
paper_ai_keywords: Personal Health Large Language Model (PH-LLM) Gemini numerical time-series personal health data sleep patterns physical activity physiological responses personalized insights and recommendations domain-specific rubrics expert domain knowledge self-reported sleep outcomes case studies multimedia encoding representations specialized discriminative models contextualizing physiological data