yang-ai-lab/HEARTS
HEARTS: Benchmarking LLM Reasoning on Health Time Series
This project helps healthcare researchers and AI practitioners evaluate how well large language models (LLMs) can interpret complex health-related data over time. You provide health time-series datasets, and it assesses the LLM's ability to perform tasks like identifying health events, predicting future trends, and analyzing patient trajectories. The output is a performance benchmark showing how accurately different LLMs reason about diverse health scenarios.
Use this if you need to rigorously test and compare the reasoning capabilities of various LLM agents on real-world health time-series data, covering a wide range of medical domains and signal types.
Not ideal if you are looking for a tool to develop specialized, non-LLM time-series models or if your focus is solely on simple data forecasting without complex reasoning.
Stars
27
Forks
3
Language
Python
License
—
Category
Last pushed
Mar 16, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/llm-tools/yang-ai-lab/HEARTS"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
Sam-Osian/PFD-toolkit
Analyse Prevention of Future Death (PFD) reports with AI
SmartFlowAI/EmoLLM
心理健康大模型 (LLM x Mental Health), Pre & Post-training & Dataset & Evaluation & Depoly & RAG, with...
AI-in-Health/MedLLMsPracticalGuide
[Nature Reviews Bioengineering🔥] Application of Large Language Models in Medicine. A curated...
Zlasejd/HuangDI
黄帝(Huang-Di)模型仓库，基于Ziya-LLaMA-13B-V1的中医古籍知识问答大模型。
mims-harvard/Madrigal
Madrigal: Multimodal AI predicts clinical outcomes of drug combinations from preclinical data