Accepted for/Published in: JMIR AI
Date Submitted: Sep 22, 2022
Open Peer Review Period: Sep 22, 2022 - Nov 17, 2022
Date Accepted: Apr 8, 2023
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Extraction of Radiological Characteristics from Free-Text Imaging Reports Utilizing Natural Language Processing Among Patients with Ischemic and Hemorrhagic Stroke
ABSTRACT
Background:
Neuroimaging is the gold standard diagnostic modality for all suspected stroke patients. However, the unstructured nature of imaging reports remains a major challenge to extracting useful information from electronic health records (EHR) systems. Despite the increasing adoption of natural language processing (NLP) for radiology reports, information extraction for many stroke imaging features has not been systematically evaluated.
Objective:
In this study, we propose an NLP pipeline, which adopts the state-of-the-art ClinicalBERT model with domain-specific pre-training to extract 13 stroke imaging features from head computed tomography (CT) imaging notes.
Methods:
We utilized the model to generate structured datasets with information on the presence or absence of common stroke features for 24,924 stroke patients. We compared the survival characteristics of patients with and without features of severe stroke (midline shift, perihematomal edema, or mass effect) using the Kaplan-Meier curve and log-rank test.
Results:
Pre-trained on 82,073 head CT notes with 61 million words and fine-tuned on 200 annotated notes, our HeadCT_BERT model achieved an average Area Under Receiver Operating Characteristic curve (AUROC) of 0.9831, F1 score of 0.8683, and accuracy of 97%. Among patients with acute ischemic stroke, admissions with any severe stroke feature in initial imaging notes were associated with lower probability of survival (P-value < .001).
Conclusions:
Our proposed NLP pipeline achieved high performance and has the potential to improve medical research and patient safety.
Citation
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Copyright
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