Development and Validation of MicrobEx: an Open-Source Package for Microbiology Culture Concept Extraction

TL;DR

MicrobEx is an open-source NLP tool that accurately extracts microbiology culture information from free-text reports, facilitating secondary clinical data use and supporting various healthcare applications.

Contribution

This paper introduces MicrobEx, a rule-based NLP package that reliably extracts culture positivity and bacteria identification from microbiology reports across multiple institutions.

Findings

F-1 scores >0.95 on classification tasks

Validated across four institutions

High accuracy in extracting microbiology data

Abstract

Microbiology culture reports contain critical information for important clinical and public health applications. However, microbiology reports often have complex, semi-structured, free-text data that present a barrier for secondary use. Here we present the development and validation of an open-source package designed to ingest free-text microbiology reports, determine whether the culture is positive, and return a list of SNOMED-CT mapped bacteria. Our rule-based natural language processing algorithm was developed using microbiology reports from two different electronic health record systems in a large healthcare organization, and then externally validated on the reports of two other institutions with manually-extracted results as a benchmark. Our algorithm achieved F-1 scores >0.95 on all classification tasks across both validation sets. Our concept extraction Python package, MicrobEx, is designed to be reused and adapted to individual institutions as an upstream process for other clinical applications, such as machine learning studies, clinical decision support, and disease surveillance systems.