Faculty, Staff and Student Publications
Publication Date
7-1-2022
Journal
JAMIA Open
Abstract
OBJECTIVE: Scanned documents in electronic health records (EHR) have been a challenge for decades, and are expected to stay in the foreseeable future. Current approaches for processing include image preprocessing, optical character recognition (OCR), and natural language processing (NLP). However, there is limited work evaluating the interaction of image preprocessing methods, NLP models, and document layout.
MATERIALS AND METHODS: We evaluated 2 key indicators for sleep apnea, Apnea hypopnea index (AHI) and oxygen saturation (SaO
RESULTS: Our proposed method using ClinicalBERT reached an AUROC of 0.9743 and document accuracy of 94.76% for AHI, and an AUROC of 0.9523 and document accuracy of 91.61% for SaO
DISCUSSION: There are multiple, inter-related steps to extract meaningful information from scanned reports. While it would be infeasible to experiment with all possible option combinations, we experimented with several of the most critical steps for information extraction, including image processing and NLP. Given that scanned documents will likely be part of healthcare for years to come, it is critical to develop NLP systems to extract key information from this data.
CONCLUSION: We demonstrated the proper use of image preprocessing and document layout could be beneficial to scanned document processing.
DOI
10.1093/jamiaopen/ooac045
PMID
35702624
PMCID
PMC9188320
PubMedCentral® Posted Date
6-11-2022
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
