# Intelligent Word Embeddings of Free-Text Radiology Reports

arXiv: https://arxiv.org/abs/1711.06968 · HTML: https://mlsanity.com/paper/1711.06968/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2017.

## Built on by (most cited first, 1 of 1)

- [Beyond Accuracy: Measuring Representation Capacity of Embeddings to Preserve Structural and Contextual Information](https://mlsanity.com/paper/2309.11294/) (2023)

## Abstract (arXiv)

Radiology reports are a rich resource for advancing deep learning applications in medicine by leveraging the large volume of data continuously being updated, integrated, and shared. However, there are significant challenges as well, largely due to the ambiguity and subtlety of natural language. We propose a hybrid strategy that combines semantic-dictionary mapping and word2vec modeling for creating dense vector embeddings of free-text radiology reports. Our method leverages the benefits of both semantic-dictionary mapping as well as unsupervised learning. Using the vector representation, we automatically classify the radiology reports into three classes denoting confidence in the diagnosis of intracranial hemorrhage by the interpreting radiologist. We performed experiments with varying hyperparameter settings of the word embeddings and a range of different classifiers. Best performance achieved was a weighted precision of 88% and weighted recall of 90%. Our work offers the potential to leverage unstructured electronic health record data by allowing direct analysis of narrative clinical notes.