PyHealth

Targets PyHealth 2.0.2, verified against its released wheel, current official documentation, and CPU execution on Python 3.12/Torch 2.7.1. PyHealth's pipeline is Dataset -> Task -> Model -> Trainer -> Metrics; its 1.x and 2.x interfaces differ. Do not combine legacy Visit examples with the 2.x event/processor API.

When to use

Use for clinical prediction with PyHealth, including EHR sequences, physiological signals, imaging tasks, or medical-code lookup. Establish the cohort, prediction time, observation window, outcome horizon, and unit of evaluation before modeling. For general tabular learning without a PyHealth dataset/task, this skill is optional.

Install and smoke-test

uv run --no-project --isolated --python 3.12 --with pyhealth==2.0.2 python assets/starter_pipeline.py --demo --epochs 1

Run that command from the skill directory, or use the absolute path to the asset. It trains on invented in-memory records and exercises patient splitting, metrics, and best-checkpoint restoration. It provides no evidence of clinical performance. See installation for project setup and device options.

Workflow

1. Inspect the installed version and dataset configuration. Both MIMIC-III and MIMIC-IV use lowercase table selectors in 2.0.2. MIMIC-III files remain uppercase.
2. Check task semantics and required tables. MortalityPredictionMIMIC3 predicts death in the next admission, excludes the last admission, and requires diagnoses, procedures and prescriptions in the current admission. It is not a current-stay early-warning model. Missing/invalid next-admission mortality flags are assigned zero upstream; audit this before using real data.
3. Create supervised samples. base.set_task(task) returns a processed dataset with input/output schemas. Inspect raw task output as well as processed samples.
4. Partition by patient and verify overlap, class counts and observation windows. split_by_patient(..., seed=42) is random, not chronological or stratified. It does not prevent within-visit temporal leakage or preprocessing leakage.
5. Choose a schema-compatible model and run one batch before training. Construct it from the training sample dataset. Transformer uses embedding_dim, not hidden_dim; arguments are model-specific.
6. Declare validation metrics and the exact monitor. Supply metrics=[...] to Trainer; the monitor must be a returned key. An absent key raises an error. Use monitor_criterion="min" for loss, "max" for AUC/accuracy.
7. Evaluate the held-out test set once the model choice is fixed. Report prevalence, patient counts, discrimination, calibration, threshold policy and uncertainty as appropriate. PR-AUC's no-skill reference depends on prevalence, not a universal 0.5.

MIMIC-III prototype

This is the 2.0.2 interface shape; the starter adds partition and label checks. Use a local authorized root for real data. The public bucket is synthetic data.

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

base = MIMIC3Dataset(
    root="https://storage.googleapis.com/pyhealth/Synthetic_MIMIC-III/",
    tables=["diagnoses_icd", "procedures_icd", "prescriptions"],
    cache_dir="./cache/mimic3", num_workers=1, dev=True,
)
samples = base.set_task(MortalityPredictionMIMIC3())
train, val, test = split_by_patient(samples, [0.6, 0.2, 0.2], seed=42)
loaders = [get_dataloader(part, batch_size=16, shuffle=(i == 0))
           for i, part in enumerate((train, val, test))]
model = Transformer(dataset=train, embedding_dim=8)
trainer = Trainer(model=model, metrics=["accuracy"], device="cpu")
trainer.train(train_dataloader=loaders[0], val_dataloader=loaders[1],
              epochs=1, monitor="accuracy", monitor_criterion="max")
print(trainer.evaluate(loaders[2]))

The public synthetic task with dev=True (up to 1000 patients) produced only 20 samples (18 negative, 2 positive) in the review run. Accuracy here checks execution only; random splits can lack a class, so this is unsuitable for reliable AUC estimation.

set_task fits processors before this split. This prototype therefore learns its vocabulary from the whole cohort. For strict held-out evaluation, partition raw patients first, fit processors on training samples only, then reuse them for validation/test; see examples. Learned adjacency matrices (e.g. GAMENet) must also use training records only.

Critical API and scientific checks

• MIMIC-IV: MIMIC4Dataset(ehr_root=..., ehr_tables=[...]); the simpler MIMIC4EHRDataset(root=..., tables=[...]) is also available. The root contains both hosp/ and icu/, not just hosp/.
• Caches exist by default. cache_dir=None selects the user cache directory; a supplied path selects its root. Cache identity does not hash raw file contents or custom task source. Use a fresh cache root after changing data/config/task code.
• Patient access: patient.get_events(event_type=..., filters=[(...)]), not patient.visits or visit.get_code_list(...).
• Patient independence: visit-level random splitting can put one patient's admissions in multiple partitions. Choose the split to match the deployment claim.
• Outcome availability: discharge diagnoses and notes are unavailable for many early prediction times. Feature timestamps and recording/store times both matter.
• Clinical interpretation: attention weights and DDI penalties are modeling tools; they do not establish causal explanation, prescribing safety or deployment readiness.
• Network resources: medical-code tables download on first use and are cached. Mapping may be one-to-many or empty; retain coding-system version and provenance.

Reference files

Need	Read
Dependencies, devices, restricted data and caches	installation
Dataset classes, constructors, event access and splitting	datasets
Task schemas, label semantics and custom tasks	tasks
Model compatibility and training contracts	models
Code lookup, mappings and tokenizer shapes	medical codes
Adaptable recipes and train-only preprocessing	examples