Named Entity Recognition in Unstructured Medical Text Documents

TL;DR

This study evaluates the effectiveness of OpenNLP and spaCy for named entity recognition to de-identify sensitive information in medical examination reports, achieving high accuracy with spaCy.

Contribution

It compares the performance of two NLP toolkits for PII removal in medical texts, highlighting spaCy's superior results with a specific training split.

Findings

Both platforms achieve high de-identification performance (f-measure > 0.9)

spaCy trained with 70-30 split performs best

Effective PII removal in medical reports using NER tools

Abstract

Physicians provide expert opinion to legal courts on the medical state of patients, including determining if a patient is likely to have permanent or non-permanent injuries or ailments. An independent medical examination (IME) report summarizes a physicians medical opinion about a patients health status based on the physicians expertise. IME reports contain private and sensitive information (Personally Identifiable Information or PII) that needs to be removed or randomly encoded before further research work can be conducted. In our study the IME is an orthopedic surgeon from a private practice in the United States. The goal of this research is to perform named entity recognition (NER) to identify and subsequently remove/encode PII information from IME reports prepared by the physician. We apply the NER toolkits of OpenNLP and spaCy, two freely available natural language processing platforms, and compare their precision, recall, and f-measure performance at identifying five categories of PII across trials of randomly selected IME reports using each models common default parameters. We find that both platforms achieve high performance (f-measure > 0.9) at de-identification and that a spaCy model trained with a 70-30 train-test data split is most performant.