PyHealth
A Python toolkit for building, testing, and deploying clinical deep learning models on healthcare datasets.
Accelerate clinical predictive modeling using built-in healthcare datasets, medical codes, and 33+ models.
PyHealth is an open-source Python library designed to simplify deep learning workflows on clinical dataset formats like MIMIC, eICU, and OMOP. It offers a structured 5-stage pipeline with built-in medical code processing, standard predictive tasks, and model trainers. Medical AI researchers and clinical data scientists should use it to build reproducible predictive healthcare models.
What it is
PyHealth provides a modular framework covering data processing, dataset mapping, task definition, model selection, and evaluation. It features specialized parsers for EHR systems and medical coding standards alongside over 33 pre-built neural network architectures. Data processing routines run up to 3x faster than standard pandas operations for quick model experimentation.
Who it's for
It suits machine learning researchers, clinical data scientists, and healthcare software engineers who need to train deep learning models on EHR data. It is not intended for non-technical clinicians seeking a no-code interface or drag-and-drop diagnostic software.
Key features
Five-Stage Healthcare ML Pipeline
Structures the workflow across data loading, dataset creation, task definition, model building, and training.
Native Clinical Format Support
Parses complex electronic health record formats including MIMIC, eICU, and standardized OMOP datasets.
Pre-Built Deep Learning Models
Includes over 33 specialized neural architectures designed for healthcare predictive tasks.
Medical Code Handling Utilities
Provides built-in mapping and preprocessing for complex clinical coding systems.
Up and running in 3 steps
Our honest verdict
What's great
- Includes specialized parsers for popular EHR databases like MIMIC and OMOP
- Offers 33+ pre-built deep learning models tailored for clinical prediction
- Runs data processing routines approximately three times faster than pandas
- Follows a clean five-stage pipeline for structured healthcare AI research
Where it falls short
- Requires strict Python 3.12 or 3.13 environments, limiting older setup compatibility
- Demands significant GPU compute resources when training deep learning models on large datasets
- Requires Python programming expertise and machine learning knowledge to operate effectively
Pricing & license
PyHealth is completely free and open-source under the MIT license. There are no paid hosted tiers, so your only expense is the local or cloud GPU compute required to run models.
Open remote AI-training roles
874 open remote AI-training roles, updated November 15, 2026; most pay $20–$170 an hour. Zekai lists them free; the application goes to the employer’s platform.
Alternatives
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FAQ
What Python versions are required for PyHealth 2.0?
PyHealth 2.0 strictly requires Python 3.12 or Python 3.13.
Which clinical databases are natively supported?
It provides built-in parsers for EHR databases such as MIMIC, eICU, and OMOP.
