PyHealth 临床机器学习研究

references/installation.md

Reviewed for the released PyHealth 2.0.2 wheel on 2026-09-30. Its metadata requires Python >=3.12,<3.14 and torch~=2.7.1, which excludes Torch 2.8 and later. The tested macOS CPU environment resolved Torch 2.7.1 and torchvision 0.22.1. Do not independently upgrade to the newest Torch or copy CUDA 12.1 commands from older PyHealth examples. The release metadata is authoritative for this pin; /en/latest/ documentation may describe newer source.

Project setup

uv init --python 3.12 my-pyhealth-project
cd my-pyhealth-project
uv add 'pyhealth==2.0.2'
uv run python -c 'import torch; import importlib.metadata as m; print(m.version("pyhealth"), torch.__version__)'

Keep the generated lockfile. To run independently of the surrounding project's Python requirement or dependencies:

uv run --no-project --isolated --python 3.12 --with pyhealth==2.0.2 python train.py

PyHealth 1.x has different datasets, task functions, schemas and model arguments. Treat an existing 1.x environment as a separate migration; these examples target 2.0.2 and do not claim legacy compatibility.

CPU, CUDA and MPS

Start with Trainer(..., device="cpu"). GPU selection at runtime does not determine which wheel the installer downloads. For Linux CUDA/ROCm, choose a supported Torch 2.7.1 / torchvision 0.22.1 pair from the official previous-version installer, and configure the package index appropriately for that environment. CUDA installation was not tested in this review.

On Apple Silicon, check torch.backends.mps.is_available() before explicitly passing device="mps"; the trainer's automatic device choice is CUDA-or-CPU. MPS operation coverage is model-dependent and was not tested here. Optional graph models may need pyhealth[graph]==2.0.2; pretrained text/image models can require separate downloads.

Data access and layout

The public PyHealth Synthetic_MIMIC-III bucket is an unauthenticated collection of CSV files, not a clinical production dataset or JSON API: https://storage.googleapis.com/pyhealth/Synthetic_MIMIC-III/. The 2.0.2 loader tries configured .csv.gz files and then .csv alternatives.

Real MIMIC and eICU require the relevant PhysioNet access conditions (credentialing, training and the data use agreement). Confirm access and release-specific conditions on the dataset landing page; download through an authorized channel and pass a local path. Do not embed access credentials in code.

For MIMIC-IV the root must contain hosp/ and icu/ because the default config loads hosp/patients.csv.gz, hosp/admissions.csv.gz, icu/icustays.csv.gz plus requested clinical tables. PyHealth's bundled MIMIC-IV config is labeled 2.2; compatibility with all newer PhysioNet releases has not been established here.

from pyhealth.datasets import MIMIC4EHRDataset
base = MIMIC4EHRDataset(
    root="./data/mimic-iv",  # contains hosp/ and icu/
    tables=["diagnoses_icd", "procedures_icd", "prescriptions"],
    cache_dir="./cache/mimic4", num_workers=1,
)

This constructor was checked against released source; restricted data parsing was not executed. MIMIC-III uses lowercase selectors with uppercase source filenames. OMOP uses its own bundled config: inspect it before assuming a particular CDM export naming convention.

Caching and small runs

BaseDataset(cache_dir=None) uses a persistent platform-specific user cache. An explicit cache root gains a dataset UUID subdirectory; task/schema/processor caches live below it. It is incorrect that omitting cache_dir always reparses. The dataset cache key includes root, tables, dataset name and dev, but not the raw file contents or full config contents. A task source edit need not invalidate the task cache either. Choose a new cache root after changing those inputs and record raw-data checksums/release and task version with each run.

dev=True limits BaseDataset processing to 1000 patients; it is not a guarantee that only 1000 patients' CSV bytes are downloaded or scanned. Not every wrapper exposes it: SleepEDFDataset, for example, has no dev or cache_dir parameter. Use a main() guard for executable examples because dataset processing can spawn workers on macOS/Windows. num_workers=1 is a useful initial setting, not a guarantee that every upstream library avoids subprocesses.