Tutorials

The easiest way to get familiar with ehrapy is to follow along with our tutorials. Many are also designed to work seamlessly in Binder, a free cloud computing platform.

Note

For questions about the usage of ehrapy use Github Discussions.

Quick start

Introduction to ehrapy

MIMIC-II IAC Introduction

MIMIC-II IAC Patient Fate

MIMIC-II Survival Analysis

MIMIC-II Causal Inference

Extracting from free text with MedCAT

Using ehrapy with machine learning

Ontology mapping

FHIR example

Cohort Tracking

Detection and dealing with bias in EHR data

Glossary

AnnData

AnnData is short for Annotated Data and is the primary datastructure that ehrapy uses. It is based on the principle of a single Numpy matrix X embraced by two Pandas Dataframes. All rows are called observations (in our case patients/patient visits or similar) and the columns are known as variables (any feature such as e.g. age, B12 level or similar). For a more in depth introduction please read the AnnData paper.

scanpy

The implementation of ehrapy is based on scanpy, a framework to analyze single-cell sequencing data. ehrapy reuses the implemented algorithms in scanpy and wraps them for simple access. For a more in depth introduction please read the Scanpy paper.