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Clinical language search algorithm from free-text: facilitating appropriate imaging.
Chaudhari, Gunvant R; Chillakuru, Yeshwant R; Chen, Timothy L; Pedoia, Valentina; Vu, Thienkhai H; Hess, Christopher P; Seo, Youngho; Sohn, Jae Ho.
Afiliação
  • Chaudhari GR; Center for Intelligent Imaging, Radiology and Biomedical Imaging, University of California San Francisco (UCSF), 505 Parnassus Ave, San Francisco, CA, 94143, USA.
  • Chillakuru YR; Center for Intelligent Imaging, Radiology and Biomedical Imaging, University of California San Francisco (UCSF), 505 Parnassus Ave, San Francisco, CA, 94143, USA.
  • Chen TL; George Washington School Medicine and Health Sciences, 2300 I St NW, Washington, DC, 20052, USA.
  • Pedoia V; Center for Intelligent Imaging, Radiology and Biomedical Imaging, University of California San Francisco (UCSF), 505 Parnassus Ave, San Francisco, CA, 94143, USA.
  • Vu TH; Illinois School of Medicine, 1853 W Polk St, Chicago, IL, 60612, USA.
  • Hess CP; Center for Intelligent Imaging, Radiology and Biomedical Imaging, University of California San Francisco (UCSF), 505 Parnassus Ave, San Francisco, CA, 94143, USA.
  • Seo Y; Center for Intelligent Imaging, Radiology and Biomedical Imaging, University of California San Francisco (UCSF), 505 Parnassus Ave, San Francisco, CA, 94143, USA.
  • Sohn JH; Center for Intelligent Imaging, Radiology and Biomedical Imaging, University of California San Francisco (UCSF), 505 Parnassus Ave, San Francisco, CA, 94143, USA.
BMC Med Imaging ; 22(1): 18, 2022 02 04.
Article em En | MEDLINE | ID: mdl-35120466
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 a dataset of simulated simple and complex indications for each AC document (n = 410) and another with clinical indications from randomly sampled radiology reports (n = 100). We compare our algorithm to a custom google search engine.

RESULTS:

On the simulated indications, our algorithm ranked ground truth documents as top 3 for 98% of simple queries and 85% of complex queries. Similarly, on the randomly sampled radiology report dataset, the algorithm ranked 86% of indications with a single match as top 3. Vague and distracting phrases present in the free-text indications were main sources of errors. Our algorithm provides more relevant results than a custom Google search engine, especially for complex queries.

CONCLUSIONS:

We have developed and evaluated an NLP algorithm that matches clinical indications to appropriate AC guidelines. This approach can be integrated into imaging ordering systems for automated access to guidelines.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Radiologia / Processamento de Linguagem Natural / Diagnóstico por Imagem Tipo de estudo: Diagnostic_studies / Guideline / Prognostic_studies Limite: Adolescent / Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Radiologia / Processamento de Linguagem Natural / Diagnóstico por Imagem Tipo de estudo: Diagnostic_studies / Guideline / Prognostic_studies Limite: Adolescent / Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Ano de publicação: 2022 Tipo de documento: Article