Agent-Skills.md

Agent Skills: PyHealth

Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN).

UncategorizedID: k-dense-ai/claude-scientific-skills/pyhealth

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k-dense-ai/claude-scientific-skills
K-Dense-AILicense: MIT
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scientific-skills/pyhealth/SKILL.md

Skill Metadata

Name
pyhealth
Description
Build clinical/healthcare deep-learning pipelines with PyHealth — loading EHR/signal/imaging datasets (MIMIC-III/IV, eICU, OMOP, SleepEDF, ChestXray14, EHRShot), defining tasks (mortality, readmission, length-of-stay, drug recommendation, sleep staging, ICD coding, EEG events), instantiating models (Transformer, RETAIN, GAMENet, SafeDrug, MICRON, StageNet, AdaCare, CNN/RNN/MLP), training with the PyHealth Trainer, computing clinical metrics, and using medical code utilities (ICD/ATC/NDC/RxNorm lookup and cross-mapping). Use this skill whenever the user mentions PyHealth, MIMIC, eICU, OMOP, EHR modeling, clinical prediction, drug recommendation, sleep staging, medical code mapping, ICD/ATC codes, or any healthcare ML pipeline that fits the dataset → task → model → trainer → metrics pattern, even if "PyHealth" isn't named explicitly.

PyHealth

PyHealth (https://pyhealth.dev/) is a Python toolkit for clinical deep learning. It provides a unified, modular pipeline across electronic health records (EHR), physiological signals, and medical imaging.

The library is built around a 5-stage pipeline — Dataset → Task → Model → Trainer → Metrics — where each stage is replaceable and the interfaces between stages are stable. Code that follows this pipeline shape composes well; code that bypasses it usually fights the library.

When to use this skill

Use this skill whenever the user is doing clinical/healthcare ML and any of the following are true:

  • They mention PyHealth, MIMIC-III/IV, eICU, OMOP-CDM, EHRShot, SleepEDF, SHHS, ISRUC, COVID19-CXR, ChestX-ray14, TUEV/TUAB.
  • They want to predict mortality, readmission, length of stay, drug recommendations, sleep stages, ICD codes, EEG events, or de-identification.
  • They need to look up or cross-map medical codes (ICD-9-CM, ICD-10-CM, ATC, NDC, RxNorm, CCS).
  • They have EHR-shaped data and want to train a clinical model without writing the plumbing themselves.

PyHealth is the right tool when the workflow fits its 5 stages. If the user just wants generic PyTorch on tabular data, this skill is not necessary.

Installation (uv)

PyHealth 2.0 requires Python ≥ 3.12, < 3.14. Use uv for environment management — it's faster and reproducible.

# Create a project with the right Python
uv init my-pyhealth-project
cd my-pyhealth-project
uv python pin 3.12

# Add PyHealth (this also pulls in PyTorch and friends)
uv add pyhealth

# Run scripts inside the env
uv run python train.py

For a one-off script without a project, use uv run --with pyhealth python script.py. For the legacy 1.x line (Python 3.9+), uv add pyhealth==1.16. Detailed install notes, MIMIC access, and GPU/CPU device tips are in references/installation.md.

The 5-stage pipeline

A complete pipeline is typically <20 lines. This is the canonical shape — start here and modify pieces:

from pyhealth.datasets import MIMIC3Dataset, split_by_patient, get_dataloader
from pyhealth.tasks import MortalityPredictionMIMIC3
from pyhealth.models import Transformer
from pyhealth.trainer import Trainer
from pyhealth.metrics.binary import binary_metrics_fn

# 1. Dataset — raw patient registry
base = MIMIC3Dataset(
    root="https://storage.googleapis.com/pyhealth/Synthetic_MIMIC-III/",
    tables=["DIAGNOSES_ICD", "PROCEDURES_ICD", "PRESCRIPTIONS"],
)

# 2. Task — converts patients into supervised samples
samples = base.set_task(MortalityPredictionMIMIC3())

# 3. Split + DataLoaders (split by patient to avoid leakage)
train_ds, val_ds, test_ds = split_by_patient(samples, [0.8, 0.1, 0.1])
train_loader = get_dataloader(train_ds, batch_size=32, shuffle=True)
val_loader   = get_dataloader(val_ds,   batch_size=32, shuffle=False)
test_loader  = get_dataloader(test_ds,  batch_size=32, shuffle=False)

# 4. Model — must be passed the SampleDataset, not the BaseDataset
model = Transformer(dataset=samples)

# 5. Train + evaluate
trainer = Trainer(model=model)
trainer.train(
    train_dataloader=train_loader,
    val_dataloader=val_loader,
    epochs=50,
    monitor="pr_auc",
)

y_true, y_prob, _ = trainer.inference(test_loader)
print(binary_metrics_fn(y_true, y_prob, metrics=["pr_auc", "roc_auc"]))

A copy-pasteable starter is in assets/starter_pipeline.py.

Critical things to get right

These are the mistakes that PyHealth code most commonly trips on. Internalize them before writing pipelines:

  1. Models take a SampleDataset, not a BaseDataset. MIMIC3Dataset(...) returns a BaseDataset (a queryable patient registry). Only after .set_task(task) do you get a SampleDataset, which is what models, splitters, and DataLoaders expect. If you pass base to a model, it will fail or behave wrong.

  2. Always split by patient (or visit), not by sample. Random sample-level splits leak information across train/test because the same patient can appear in both. Use split_by_patient for patient-level prediction, split_by_visit only when visits are independent.

  3. Match the task to the dataset. Tasks are dataset-specific: MortalityPredictionMIMIC3 won't work on MIMIC-IV — use MortalityPredictionMIMIC4 or InHospitalMortalityMIMIC4. The full mapping is in references/tasks.md.

  4. Pick monitor to match the task type. For binary classification use "pr_auc" or "roc_auc". For multilabel (drug rec) use "pr_auc_samples" or "jaccard_samples". For multiclass use "accuracy" or "f1_macro". Wrong monitor → checkpoint selection saves the wrong epoch.

  5. MIMIC-IV uses ehr_root=, not root=. This is the one inconsistency in the dataset constructors.

  6. For reproducible work, point cache_dir= somewhere persistent. PyHealth caches the parsed dataset; without cache_dir, you re-parse every run.

How to use this skill

PyHealth has a large API surface — there's no point loading it all at once. Read the reference file that matches the user's task:

| If the user is asking about… | Read | |---|---| | Installing, env setup, MIMIC access, GPU | references/installation.md | | Which dataset class to use, loading patterns, splitting | references/datasets.md | | What prediction task to choose (mortality, readmission, drug rec, sleep…) | references/tasks.md | | Picking a model architecture, model-specific arguments | references/models.md | | Looking up or cross-mapping ICD/ATC/NDC/RxNorm/CCS codes, tokenizers | references/medcode.md | | End-to-end recipes for common scenarios | references/examples.md |

For multi-step tasks (e.g., "build a drug recommendation pipeline on MIMIC-IV"), read tasks.md + models.md + examples.md together — they cross-reference each other.

A note on style

Write minimal, idiomatic PyHealth. The library is opinionated; lean into its abstractions instead of reimplementing them in raw PyTorch. If you find yourself writing a custom training loop, ask whether Trainer would do the job — it almost always will, and it handles checkpointing, logging, and best-model selection for free.

When the user has private MIMIC access, point them at the local CSV root; for demos and learning, the synthetic MIMIC-III bucket (https://storage.googleapis.com/pyhealth/Synthetic_MIMIC-III/) is fine and works without credentialing.