Abstract
OBJECTIVE: Microbiology culture reports contain critical information for important clinical and public health applications. However, microbiology reports often have complex, semistructured, 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 Systemized Nomenclature of Medicine (SNOMED)-CT mapped bacteria. MATERIALS AND METHODS: Our concept extraction Python package, MicrobEx, is built upon a rule-based natural language processing algorithm and was developed using microbiology reports from 2 different electronic health record systems in a large healthcare organization, and then externally validated on the reports of 2 other institutions with manually reviewed results as a benchmark. RESULTS: MicrobEx achieved F1 scores >0.95 on all classification tasks across 2 independent validation sets with minimal customization. Additionally, MicrobEx matched or surpassed our MetaMap-based benchmark algorithm performance across positive culture classification and species capture classification tasks. DISCUSSION: Our results suggest that MicrobEx can be used to reliably estimate binary bacterial culture status, extract bacterial species, and map these to SNOMED organism observations when applied to semistructured, free-text microbiology reports from different institutions with relatively low customization. CONCLUSION: MicrobEx offers an open-source software solution (available on both GitHub and PyPI) for bacterial culture status estimation and bacterial species extraction from free-text microbiology reports. The package was designed to be reused and adapted to individual institutions as an upstream process for other clinical applications such as: machine learning, clinical decision support, and disease surveillance systems.