Literature DB >> 18178520

A unified representation of findings in clinical radiology using the UMLS and DICOM.

Valérie Bertaud1, Jérémy Lasbleiz, Fleur Mougin, Anita Burgun, Régis Duvauferrier.   

Abstract

PURPOSE: Collecting and analyzing findings constitute the basis of medical activity. Computer assisted medical activity raises the problem of modelling findings. We propose a unified representation of findings integrating the representations of findings in the GAMUTS in Radiology [M.M. Reeder, B. Felson, GAMUTS in radiology Comprehensive lists of roentgen differential diagnosis, fourth ed., 2003], the Unified Medical Language System (UMLS), and the Digital Imaging and Communication in Medicine Structured Report (DICOM-SR).
MATERIALS AND METHODS: Starting from a corpus of findings in bone and joint radiology [M.M. Reeder, B. Felson, GAMUTS in Radiology comprehensive lists of roentgen differential diagnosis, fourth ed., 2003] (3481 words), an automated mapping to the UMLS was performed with the Metamap Program. The resulting UMLS terms and Semantic Types were analyzed in order to find a generic template in accordance with DICOM-SR structure.
RESULTS: UMLS Concepts were missing for 45% of the GAMUTS findings. Three kinds of regularities were observed in the way the Semantic Types were combined: "pathological findings", "physiological findings" and "anatomical findings". A generic and original DICOM-SR template modelling finding was proposed. It was evaluated for representing GAMUTS jaws findings. 21% missing terms had to be picked up from Radlex (5%) or created (16%). DISCUSSION-
CONCLUSION: This article shows that it is possible to represent findings using the UMLS and the DICOM SR formalism with a semi-automated method. The Metamap program helped to find a model to represent the semantic structure of free texts with standardized terms (UMLS Concepts). Nevertheless, the coverage of the UMLS is not comprehensive. This study shows that the UMLS should include more technical concepts and more concepts regarding findings, signs and symptoms to be suitable for radiology representation. The semi-automated translation of the whole GAMUTS using the UMLS concepts and the DICOM SR relations could help to create or supplement the DCMR Templates and Context Groups pertaining to the description of imaging findings.

Mesh:

Year:  2008        PMID: 18178520     DOI: 10.1016/j.ijmedinf.2007.11.003

Source DB:  PubMed          Journal:  Int J Med Inform        ISSN: 1386-5056            Impact factor:   4.046


  9 in total

1.  Associating clinical archetypes through UMLS Metathesaurus term clusters.

Authors:  Leonardo Lezcano; Salvador Sánchez-Alonso; Miguel-Angel Sicilia
Journal:  J Med Syst       Date:  2010-09-09       Impact factor: 4.460

2.  Identifying QT prolongation from ECG impressions using a general-purpose Natural Language Processor.

Authors:  Joshua C Denny; Randolph A Miller; Lemuel Russell Waitman; Mark A Arrieta; Joshua F Peterson
Journal:  Int J Med Inform       Date:  2008-10-19       Impact factor: 4.046

3.  Workflow Lexicons in Healthcare: Validation of the SWIM Lexicon.

Authors:  Chris Meenan; Bradley Erickson; Nancy Knight; Jewel Fossett; Elizabeth Olsen; Prerna Mohod; Joseph Chen; Steve G Langer
Journal:  J Digit Imaging       Date:  2017-06       Impact factor: 4.056

4.  [Current reporting in radiology : what will happen tomorrow?].

Authors:  T Baumann; T Hackländer; E Kotter
Journal:  Radiologe       Date:  2014-01       Impact factor: 0.635

5.  Reviewing 741 patients records in two hours with FASTVISU.

Authors:  Jean-Baptiste Escudié; Anne-Sophie Jannot; Eric Zapletal; Sarah Cohen; Georgia Malamut; Anita Burgun; Bastien Rance
Journal:  AMIA Annu Symp Proc       Date:  2015-11-05

6.  Using the Unified Medical Language System to Expand the Operative Stress Score - First Use Case.

Authors:  Katherine M Reitz; Daniel E Hall; Myrick C Shinall; Paula K Shireman; Jonathan C Silverstein
Journal:  J Surg Res       Date:  2021-08-28       Impact factor: 2.192

7.  Lexicon for renal mass terms at CT and MRI: a consensus of the society of abdominal radiology disease-focused panel on renal cell carcinoma.

Authors:  Atul B Shinagare; Matthew S Davenport; Hyesun Park; Ivan Pedrosa; Erick M Remer; Hersh Chandarana; Ankur M Doshi; Nicola Schieda; Andrew D Smith; Raghunandan Vikram; Zhen J Wang; Stuart G Silverman
Journal:  Abdom Radiol (NY)       Date:  2020-08-18

8.  A novel data-driven workflow combining literature and electronic health records to estimate comorbidities burden for a specific disease: a case study on autoimmune comorbidities in patients with celiac disease.

Authors:  Jean-Baptiste Escudié; Bastien Rance; Georgia Malamut; Sherine Khater; Anita Burgun; Christophe Cellier; Anne-Sophie Jannot
Journal:  BMC Med Inform Decis Mak       Date:  2017-09-29       Impact factor: 2.796

9.  Semantic representation of reported measurements in radiology.

Authors:  Heiner Oberkampf; Sonja Zillner; James A Overton; Bernhard Bauer; Alexander Cavallaro; Michael Uder; Matthias Hammon
Journal:  BMC Med Inform Decis Mak       Date:  2016-01-22       Impact factor: 2.796

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.