🚀 ICML 2026 — PyHealth 2.0 What if clinical AI didn’t require a remote GPU cluster to get anything done? PyHealth 2.0 flips the default: • 💻 Run locally on a 16GB laptop — no heavyweight infra required • ⚡ Up to 39× faster, 20× lower memory vs prior pipelines • 🔄 Same workflows scale seamlessly to large remote servers when needed This isn’t just efficiency — it’s a shift in who can actually do clinical AI: → Prototype, debug, and iterate locally → Reproduce results without expensive compute → Scale to production only when necessary Under the hood, still a full-stack toolkit: • 15 datasets, 20 tasks, 25 models • Multimodal (EHR, imaging, signals) • Interpretability uncertainty (incl. conformal prediction) • 400 open-source contributors Bottom line: Clinical AI shouldn’t start with a cluster request. It should start on your laptop. 👏 Huge congrats to all my students and collaborators on this work — proud of what you’ve built. openreview.net/pdf?id=gMLVFN… Check out Pyhealth pyhealth.dev/ #ICML2026 #HealthcareAI #EfficientAI #OpenSource #Reproducibility #MachineLearning

🚀 PyHealth for Industry Practitioners: Clinical Predictive Modeling, Finally Practical We’re excited to announce PyHealth 2.0a13, a major step toward making efficient, interpretable, and cutting-edge clinical predictive modeling accessible to industry teams. With a new backend memory optimization, PyHealth now reduces RAM requirements from ~385GB to ~16GB when working with large-scale EHR datasets such as MIMIC-IV—a 24× reduction. This means many real-world clinical prediction workflows can now run on consumer hardware or laptops, not just large compute clusters. If you work in healthcare, life sciences, or clinical AI and care about: - Practical deployment (not just benchmarks) - Model transparency and interpretability - Rapid experimentation on real EHR data PyHealth is built for you. 📖 Technical details: lnkd.in/gfhUY5xN 💬 Feedback & testing: lnkd.in/gAuh7nGF TL;DR: Clinical predictive modeling on a laptop is now realistic—and we’d love your help testing the new backend. linkedin.com/posts/john-wu-c…

🚀 Join the Sunlab PyHealth Hackathon 2024! Are you passionate about Healthcare AI and open-source development? Here's your chance to make a real impact! 🏥💻 Ready to innovate? Sign up now! 👇 📝 Registration Form: forms.gle/bU8z3ih1rD2orZMq7 🔗Event link github.com/sunlabuiuc/PyHeal… We're excited to announce a week-long hackathon focused on PyHealth - an open-source framework that's revolutionizing healthcare AI development. Whether you're a seasoned ML engineer or a prospective graduate student, there's a place for you! 🗓️ Key Dates: • Oct 28: Kickoff & Tutorial (9AM-12PM CT) • Oct 29: Project Formation • Oct 30-31: Project Development • Nov 1: Final Presentations ✨ What you'll get: • Hands-on experience with PyHealth • Mentorship from healthcare AI experts • Opportunity to contribute to meaningful healthcare solutions • Collaboration with like-minded innovators • Guidance from Dr. Jimeng Sun and the Sunlab team 🎯 Project Tracks: 1. Framework Updates: Perfect for quick, impactful contributions 2. Research Paper Implementation: Dive deep into cutting-edge healthcare AI 🌟 Expert Mentors: • Zhenbang Wu - PhD researcher, Healthcare Foundation Models • John Wu - PhD researcher, Explainable Healthcare AI • Dr. Jimeng Sun - Health Innovation Professor, UIUC #HealthcareAI #OpenSource #Hackathon #PyHealth #MachineLearning #HealthTech #UIUC Want to learn more? Check out: 📚 Documentation: pyhealth.readthedocs.io 🔗 GitHub: github.com/sunlabuiuc/PyHeal…

Girls’ Education and Age at first marriage. #HealthData #PyHealth

PyHealth：ヘルスケアデータに対する予測モデルを提供するPythonライブラリ。データ前処理、予測モデリング、評価の３つのモジュールで構成。オススメのポイントは、①scikit-learnに似せたデザイン。②30以上の機械学習モデルを提供。③画像データやテキストデータにも対応。 arxiv.org/abs/2101.04209

#PyHealth is a free python package for healthcare predictive modeling. It supports step-wise building of predictive models, testing and evaluating each model step by step! Learn more here: bit.ly/3sfOxzl via @analyticsindiamag #deeplearning #datascience #ai #python

PyHealth: A Python Library for Health Predictive Models. Check this new python library specialised in AI in Healthcare. buff.ly/3svdLJH #machinelearning #pyhealth

Check out our PyHealth a deep learning library for building clinical predictive models over electronic health records. @caoxiao_danica @yzhao062 @AiLucasg

PyHealth - A Python Library for Healthcare AI github.com/yzhao062/PyHealth

PyHealth: A Python Library for Healthcare AI github.com/yzhao062/PyHealth #Python

Tobacco Lung Lab in Health Class! #healtheducation #smokerslung #risksofsmoking #PYHealth