Clinical language search algorithm from free-text: facilitating appropriate imaging.

ABSTRACT:

Background

The comprehensiveness and maintenance of the American College of Radiology (ACR) Appropriateness Criteria (AC) makes it a unique resource for evidence-based clinical imaging decision support, but it is underutilized by clinicians. To facilitate the use of imaging recommendations, we develop a natural language processing (NLP) search algorithm that automatically matches clinical indications that physicians write into imaging orders to appropriate AC imaging recommendations.

Methods

We apply a hybrid model of semantic similarity from a sent2vec model trained on 223 million scientific sentences, combined with term frequency inverse document frequency features. AC documents are ranked based on their embeddings' cosine distance to query. For model testing, we compiled

SUBMITTER: Chaudhari GR 

PROVIDER: S-EPMC8815252 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature