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Found 6 papers, 1 papers with code
Towards a Personal Health Large Language Model
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.
Transforming Wearable Data into Health Insights using Large Language Model Agents
Despite the proliferation of wearable health trackers and the importance of sleep and exercise to health, deriving actionable personalized insights from wearable data remains a challenge because doing so requires non-trivial open-ended analysis of these data.
Multimodal LLMs for health grounded in individual-specific data
To effectively solve personalized health tasks, LLMs need the ability to ingest a diversity of data modalities that are relevant to an individual's health status.
Predicting Cardiovascular Disease Risk using Photoplethysmography and Deep Learning
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.
Knowledge distillation for fast and accurate DNA sequence correction
Accurate genome sequencing can improve our understanding of biology and the genetic basis of disease.
Large-scale machine learning-based phenotyping significantly improves genomic discovery for optic nerve head morphology
A GWAS of ML-based VCDR identified 299 independent genome-wide significant (GWS; $P\leq5\times10^{-8}$) hits in 156 loci.
