Which deep learning frameworks are required?

PyHealth is built on top of PyTorch (version 1.8 or higher) and integrates seamlessly with the Python data science ecosystem.

Does it handle medical code translation?

Absolutely. It features robust mapping tools for translating between ICD-9, ICD-10, NDC, RxNorm, ATC, and other clinical coding systems.

What datasets does PyHealth support?

PyHealth supports over 10 major healthcare datasets including MIMIC-III, MIMIC-IV, eICU, OMOP, and various physiological signal datasets like SleepEDF.

Is this skill suitable for EEG or signal processing?

Yes, it includes tools for processing physiological signals like EEG and ECG, with predefined tasks like sleep staging.

Can I use this for medication recommendations?

Yes, PyHealth includes specialized models like SafeDrug and GAMENet designed specifically for medication recommendation while monitoring drug-drug interactions.

PyHealth: Healthcare AI & Clinical ML

Name: PyHealth: Healthcare AI & Clinical ML
Author: Activer007

byActiver007

Ciencia de Datos y ML

Develops, trains, and deploys specialized machine learning models for healthcare and clinical data analysis using standardized EHR datasets and medical coding systems.

Acerca de

PyHealth is a comprehensive Python toolkit designed for the complex world of healthcare AI, offering a modular 5-stage pipeline that streamlines the transition from raw clinical data to deployable models. It supports over 10 major healthcare datasets like MIMIC-IV and eICU, handles complex medical coding translations across systems like ICD and RxNorm, and provides access to state-of-the-art healthcare-specific architectures such as RETAIN and SafeDrug. Whether you are predicting patient mortality, recommending medications without drug-drug interactions, or analyzing EEG signals, PyHealth provides the specialized data structures and clinical metrics necessary for rigorous medical AI development.

Características Principales

Modular 5-stage pipeline for data loading, task definition, model selection, training, and deployment.
Support for 10+ major healthcare datasets including MIMIC-III/IV, eICU, OMOP, and sleep EEG.
0 GitHub stars
Medical coding translation across ICD-9/10, NDC, RxNorm, ATC, and CCS systems.
Predefined clinical tasks for mortality, readmission, length of stay, and drug recommendation.
Access to 33+ models including specialized architectures like RETAIN, SafeDrug, and GAMENet.

Casos de Uso

Developing medication recommendation systems that automatically avoid harmful drug-drug interactions.
Standardizing disparate medical codes across multiple datasets for longitudinal patient studies.
Predicting 30-day hospital readmission risk using EHR data and Transformer models.

Acerca de

Características Principales

Modular 5-stage pipeline for data loading, task definition, model selection, training, and deployment.
Support for 10+ major healthcare datasets including MIMIC-III/IV, eICU, OMOP, and sleep EEG.
0 GitHub stars
Medical coding translation across ICD-9/10, NDC, RxNorm, ATC, and CCS systems.
Predefined clinical tasks for mortality, readmission, length of stay, and drug recommendation.
Access to 33+ models including specialized architectures like RETAIN, SafeDrug, and GAMENet.

Casos de Uso

Developing medication recommendation systems that automatically avoid harmful drug-drug interactions.
Standardizing disparate medical codes across multiple datasets for longitudinal patient studies.
Predicting 30-day hospital readmission risk using EHR data and Transformer models.