EHRKit: A Python Natural Language Processing Toolkit for Electronic Health Record Texts

Abstract

The Electronic Health Record (EHR) is an essential part of the modern medical system and impacts healthcare delivery, operations, and research. Unstructured text is attracting much attention despite structured information in the EHRs and has become an exciting research field. The success of the recent neural Natural Language Processing (NLP) method has led to a new direction for processing unstructured clinical notes. In this work, we create a python library for clinical texts, EHRKit. This library contains two main parts: MIMIC-IIIspecific functions and tasks specific functions. The first part introduces a list of interfaces for accessing MIMIC-III NOTEEVENTS data, including basic search, information retrieval, and information extraction. The second part integrates many third-party libraries for up to 12 off-shelf NLP tasks such as named entity recognition, summarization, machine translation, etc.

FrESCO: Framework for Exploring Scalable Computational Oncology

“…Most similar to FrESCO is PyHealth (Zhao et al, 2021) though it is more broadly scoped, focusing on MIMIC (Medical Information Mart for Intensive Care), electronic intensive care unit (eICU), and observational medical outcomes partnership common data model (OMOP-CDM) databases. Biomedical libraries such as Med7 (Kormilitzin et al, 2021) and EHRkit (Li et al, 2022) focus on electronic health records in general and machine learning tasks such as named-entity recognition and document summarization. Our FrESCO library is singularly focused on cancer pathology reports and provides the model building workflow for auto-coding SEER pathology reports, which is a fundamental requirement in a clinical deployment environment (Harris et al, 2022).…”