AMIA 2021 Workshop on Clinical Information Extraction for Collaborative EHR-based Clinical Research

The following materials are designed for AMIA 2021 workshop on Clinical Information Extraction for Collaborative EHR-based Clinical Research (W26).

Handouts

The slides used in our workshop and pre-recorded videos could be downloaded from AMIA workshop Handout Downloads page.

Backbone

Backbone aims to simplify scalable ETL (Extract-Transform-Load) processes by transforming such operations into a sequence of simple user-accessible JSON configurations, with a particular focus on Healthcare NLP-related tasks.

Backbone input database URL: URL can be used as a sample input data source during our workshop session.

MedTagger

MedTagger contains a suite of programs that the Mayo Clinic NLP program developed. It includes three major components:

• MedTagger for indexing based on dictionaries
• MedTaggerIE for information extraction based on patterns
• MedTaggerML for machine learning-based named entity recognition.

Data Annotation

We offer several materials for you to experience the data annotation process in the case study section, which includes:

1. MedTator. MedTator is a serverless web tool for data annotation. We use this tool in our case study section. For more detailed documents about MedTator, you can visit MedTator Wiki
2. Annotation Guideline. The annotation guideline describes what we want to annotate in this task.
3. Annotation Schema. This file defines the entity tags and relation tags to be extracted in the annotation.
4. Sample data. The annotation_sample/ folder of this repo contains the sample data for case study. The raw_txt folder contains the raw text file, and the ann_xml folder contains the our annotated samples.