부산대학교 도서관

Background: The opioid crisis is a national public health emergency in US. Especially, prescription opioids contributed significantly to drug overdose deaths. To improve the surveillance of prescription opioid overdose, it is critical to accurately collect prescription opioid information and calculate morphine milligram equivalents (MMEs). However, plenty of detailed information is only contained in the free text Sig component of electronic health record (EHR) prescriptions and need to be extracted first. Moreover, it is also indispensable to normalize opioid information extracted from multiple heath care facilities to clinical data standards such as Fast Healthcare Interoperability Resources (FHIR) for efficient clinical decision support. However, few efforts are spent in this direction at present. Methods: In this study, we designed and implemented a system that can automatically extract opioid information from free text in Sig and map them to FHIR. The system, named as Opioid2FHIR, applied multiple natural language processing (NLP) techniques for opioid information extraction and normalization. In order to reduce manual efforts, a general-purpose medication IE model was first leveraged. Based on 1, 000 opioid prescription records randomly selected from MIMICIII, post-processing rules were designed to adapt the IE model to opioid medications. Concept normalization models were also built to transform and map the extracted medication elements to fine-granular standard concepts in FHIR. The system was evaluated on another 1, 000 opioid prescription records in MIMICIII. Results: Opioid2FHIR obtained an F-measure of 0.963 for medication information extraction and an accuracy of 0.987 for medical concept normalization. Conclusions: A clinical NLP application to EHR opioid scripts would fill a current gap in available batch script processing tools and would greatly enhance individual prescription processing limitations of prescription drug monitoring programs and clinical MME calculators.