PyHealth — healthcare AI toolkit
⚠️ AUTHORIZED USE ONLY — build, test, and validate clinical ML models with PyHealth 2.x: EHR datasets (MIMIC-III/IV, eICU, OMOP), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding (ICD, NDC, ATC), and clinical deep learning models (RETAIN, SafeDrug, GAMENet, Transformer)
- What
- ⚠️ AUTHORIZED USE ONLY — build, test, and validate clinical ML models with PyHealth 2.x: EHR datasets (MIMIC-III/IV, eICU, OMOP), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding (ICD, NDC, ATC), and clinical deep learning models (RETAIN, SafeDrug, GAMENet, Transformer)
- Cost
- Free
- Needs
- a dedicated Python 3.12 or 3.13 environment (uv venv --python 3.13) — PyHealth 2.0.2 pins numpy 2.2 / torch 2.7 / transformers 4.53; GPU recommended for deep models; your own data — MIMIC-III/IV and eICU need YOUR PhysioNet credentialed access and data use agreement (never copy restricted records into prompts or repos)
- Install
- Copy the installer prompt below into your Muse — your agent does the rest.
Curated by Skill Harbor — @alterlab-ieu's alterlab-pyhealth skill, listed here with credit to its creator (part of the AlterLab Academic Skills suite): a complete agent reference for clinical machine learning with the PyHealth 2.x healthcare AI toolkit. ⚠️ AUTHORIZED USE ONLY / ⚠️ USAGE AUTORISÉ UNIQUEMENT — MIMIC-III/IV and eICU data require the user's own PhysioNet credentialed access and a data use agreement; never copy restricted records into prompts, notebooks, or repositories the agreement does not cover. The skill covers the modular 5-stage pipeline (data loading, task definition, model selection, training with the Trainer, validation), version-gotcha documentation for the PyHealth 2.0 API rewrite (tasks as instantiable classes, explicit table lists, schema-driven models, metric strings without `_score` suffix), clinical use cases (ICU mortality prediction, safe medication recommendation, readmission prediction, sleep staging from EEG, medical code translation, clinical text to ICD coding), a complete runnable mortality-prediction workflow, and a serious clinical-validation section — calibration, conformal prediction, fairness metrics, interpretability, TRIPOD+AI reporting. Honest caveats: the models are research-grade risk estimates for qualified clinicians, not medical devices — deployment as a medical device falls under device regulation (e.g. FDA SaMD, EU MDR); deep models need thousands of patients and GPU; PyHealth 2.0.2 pins its stack (Python 3.12-3.13, numpy 2.2, torch 2.7) so it needs its own environment. MIT licensed. Skill Harbor never reviews the code, review it yourself before use. Discovered via skills.sh.
Version:
Install
Prerequisites: a dedicated Python 3.12 or 3.13 environment (uv venv --python 3.13) — PyHealth 2.0.2 pins numpy 2.2 / torch 2.7 / transformers 4.53; GPU recommended for deep models; your own data — MIMIC-III/IV and eICU need YOUR PhysioNet credentialed access and data use agreement (never copy restricted records into prompts or repos) Install "PyHealth — healthcare AI toolkit" for me. It gives my agent @alterlab-ieu's PyHealth 2.x reference: EHR dataset loaders (MIMIC-III/IV, eICU, OMOP), clinical prediction task classes (mortality, readmission, length of stay, drug recommendation), medical coding utilities (ICD/NDC/ATC/CCS via InnerMap/CrossMap), clinical deep learning models (RETAIN, SafeDrug, GAMENet, Transformer, GAT/GCN), the Trainer with metrics and monitoring, calibration/conformal-prediction/fairness/interpretability guidance, TRIPOD+AI reporting, and a complete runnable mortality-prediction workflow. Part of the AlterLab Academic Skills suite. MIT licensed. Repository: https://github.com/alterlab-ieu/alterlab-academic-skills/blob/main/skills/clinical-research/alterlab-pyhealth/SKILL.md 1. Fetch the SKILL.md file (and any helper files) from the repository path into a temporary folder and summarize what it does in one or two sentences. 2. Safety check: review the SKILL.md and scripts for anything suspicious (unexpected network calls, shell commands, credential harvesting). This repo should contain zero secrets in code — dataset access is through my own credentialed PhysioNet account. Verify that holds here; STOP on any red flag and tell me. 3. Install it as a skill: copy SKILL.md and its helper files into the agent's skills directory, in a folder named "alterlab-pyhealth". 4. Verify with no network calls: frontmatter valid, files in place. 5. Report what was installed, where, and what I still need to do myself (e.g. uv venv --python 3.13 and uv pip install "pyhealth>=2.0.2"; complete PhysioNet credentialing and sign the data use agreement before touching MIMIC/eICU; split data by patient; treat outputs as research-grade risk estimates, not clinical decisions). GitHub is optional: if I have a GitHub account or the gh CLI, you may use it; otherwise public access is fine. Never require it unless it's in the prerequisites above. Rules: don't touch anything outside the temp folder and the install target. If anything looks off, stop and ask me.
Questions
How do I install a build?
Every product page includes a copy-paste install prompt. Paste it into your Muse and it sets the build up for you — no manual configuration.
Where does my money go?
Straight to the seller. Skill Harbor never processes payments: checkout happens on the seller’s own page, usually Stripe.
What does the ✓ next to a creator’s name mean?
It means we confirmed the identity of the person behind the listing. It says nothing about the code itself — always check a build before installing it.