Details

Autor(en) / Beteiligte

• Banerjee, Imon
• Chen, Matthew C.
• Lungren, Matthew P.
• Rubin, Daniel L.

Titel

Radiology report annotation using intelligent word embeddings: Applied to multi-institutional chest CT cohort

Ist Teil von

• Journal of biomedical informatics, 2018-01, Vol.77, p.11-20

Ort / Verlag

United States: Elsevier Inc

Erscheinungsjahr

2018

Link zum Volltext

Quelle

MEDLINE

Beschreibungen/Notizen

• [Display omitted] •Unsupervised method combining neural embedding with semantic dictionary mapping.•Creates a dense vector representation of unstructured radiology reports.•Semi-automate report categorization based on diagnosis of pulmonary embolism (PE).•Lowest generalization error with highest F1 scores.•Outperformed state-of-the-art rule-based system – PEFinder.•Can be extended to a different domain with minimal human effort. We proposed an unsupervised hybrid method – Intelligent Word Embedding (IWE) that combines neural embedding method with a semantic dictionary mapping technique for creating a dense vector representation of unstructured radiology reports. We applied IWE to generate embedding of chest CT radiology reports from two healthcare organizations and utilized the vector representations to semi-automate report categorization based on clinically relevant categorization related to the diagnosis of pulmonary embolism (PE). We benchmark the performance against a state-of-the-art rule-based tool, PeFinder and out-of-the-box word2vec. On the Stanford test set, the IWE model achieved average F1 score 0.97, whereas the PeFinder scored 0.9 and the original word2vec scored 0.94. On UPMC dataset, the IWE model’s average F1 score was 0.94, when the PeFinder scored 0.92 and word2vec scored 0.85. The IWE model had lowest generalization error with highest F1 scores. Of particular interest, the IWE model (trained on the Stanford dataset) outperformed PeFinder on the UPMC dataset which was used originally to tailor the PeFinder model.