Causal Associations Among Diseases and Imaging Findings in Radiology Reports

Ronnie Sebro; Charles E. Kahn

doi:10.3233/SHTI220487

Causal Associations Among Diseases and Imaging Findings in Radiology Reports

Ronnie Sebro
, Charles E. Kahn

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

This study explored the ability to identify causal relationships between diseases and imaging findings from their co-occurrences in radiology reports. A natural language processing (NLP) system with negative-expression filtering detected positive mentions of 16,912 disorders, interventions, and imaging findings in 1,702,462 consecutive radiology reports; the 55,564 causal relations defined by the Radiology Gamuts Ontology (RGO) served as reference standard. Conditions were considered to co-occur if they were present in reports from the same patient. The φ and κ statistics both achieved AUC0.70, P<0.001 in identifying causal relationships from pairwise co-occurrence data. Analysis of radiology reports can identify a large proportion of known causal associations among diseases and imaging findings. Automated approaches hold promise to identify causal relationships among diseases and imaging findings from their co-occurrence in text-based radiology reports.

Original language	English (US)
Title of host publication	Challenges of Trustable AI and Added-Value on Health - Proceedings of MIE 2022
Editors	Brigitte Seroussi, Patrick Weber, Ferdinand Dhombres, Cyril Grouin, Jan-David Liebe, Jan-David Liebe, Jan-David Liebe, Sylvia Pelayo, Andrea Pinna, Bastien Rance, Bastien Rance, Lucia Sacchi, Adrien Ugon, Adrien Ugon, Arriel Benis, Parisis Gallos
Publisher	IOS Press BV
Pages	411-412
Number of pages	2
ISBN (Electronic)	9781643682846
DOIs	https://doi.org/10.3233/SHTI220487
State	Published - May 25 2022
Externally published	Yes
Event	32nd Medical Informatics Europe Conference, MIE 2022 - Nice, France Duration: May 27 2022 → May 30 2022

Publication series

Name	Studies in Health Technology and Informatics
Volume	294
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Conference

Conference	32nd Medical Informatics Europe Conference, MIE 2022
Country/Territory	France
City	Nice
Period	5/27/22 → 5/30/22

Keywords

Big data
Health data science
Knowledge discovery
Natural language processing
Ontologies
Radiology
Reporting

ASJC Scopus subject areas

Biomedical Engineering
Health Informatics
Health Information Management

Access to Document

10.3233/SHTI220487

Cite this

Sebro, R., & Kahn, C. E. (2022). Causal Associations Among Diseases and Imaging Findings in Radiology Reports. In B. Seroussi, P. Weber, F. Dhombres, C. Grouin, J.-D. Liebe, J.-D. Liebe, J.-D. Liebe, S. Pelayo, A. Pinna, B. Rance, B. Rance, L. Sacchi, A. Ugon, A. Ugon, A. Benis, & P. Gallos (Eds.), Challenges of Trustable AI and Added-Value on Health - Proceedings of MIE 2022 (pp. 411-412). (Studies in Health Technology and Informatics; Vol. 294). IOS Press BV. https://doi.org/10.3233/SHTI220487

Causal Associations Among Diseases and Imaging Findings in Radiology Reports. / Sebro, Ronnie; Kahn, Charles E.
Challenges of Trustable AI and Added-Value on Health - Proceedings of MIE 2022. ed. / Brigitte Seroussi; Patrick Weber; Ferdinand Dhombres; Cyril Grouin; Jan-David Liebe; Jan-David Liebe; Jan-David Liebe; Sylvia Pelayo; Andrea Pinna; Bastien Rance; Bastien Rance; Lucia Sacchi; Adrien Ugon; Adrien Ugon; Arriel Benis; Parisis Gallos. IOS Press BV, 2022. p. 411-412 (Studies in Health Technology and Informatics; Vol. 294).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Sebro, R & Kahn, CE 2022, Causal Associations Among Diseases and Imaging Findings in Radiology Reports. in B Seroussi, P Weber, F Dhombres, C Grouin, J-D Liebe, J-D Liebe, J-D Liebe, S Pelayo, A Pinna, B Rance, B Rance, L Sacchi, A Ugon, A Ugon, A Benis & P Gallos (eds), Challenges of Trustable AI and Added-Value on Health - Proceedings of MIE 2022. Studies in Health Technology and Informatics, vol. 294, IOS Press BV, pp. 411-412, 32nd Medical Informatics Europe Conference, MIE 2022, Nice, France, 5/27/22. https://doi.org/10.3233/SHTI220487

Sebro R, Kahn CE. Causal Associations Among Diseases and Imaging Findings in Radiology Reports. In Seroussi B, Weber P, Dhombres F, Grouin C, Liebe JD, Liebe JD, Liebe JD, Pelayo S, Pinna A, Rance B, Rance B, Sacchi L, Ugon A, Ugon A, Benis A, Gallos P, editors, Challenges of Trustable AI and Added-Value on Health - Proceedings of MIE 2022. IOS Press BV. 2022. p. 411-412. (Studies in Health Technology and Informatics). doi: 10.3233/SHTI220487

Sebro, Ronnie ; Kahn, Charles E. / Causal Associations Among Diseases and Imaging Findings in Radiology Reports. Challenges of Trustable AI and Added-Value on Health - Proceedings of MIE 2022. editor / Brigitte Seroussi ; Patrick Weber ; Ferdinand Dhombres ; Cyril Grouin ; Jan-David Liebe ; Jan-David Liebe ; Jan-David Liebe ; Sylvia Pelayo ; Andrea Pinna ; Bastien Rance ; Bastien Rance ; Lucia Sacchi ; Adrien Ugon ; Adrien Ugon ; Arriel Benis ; Parisis Gallos. IOS Press BV, 2022. pp. 411-412 (Studies in Health Technology and Informatics).

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title = "Causal Associations Among Diseases and Imaging Findings in Radiology Reports",

abstract = "This study explored the ability to identify causal relationships between diseases and imaging findings from their co-occurrences in radiology reports. A natural language processing (NLP) system with negative-expression filtering detected positive mentions of 16,912 disorders, interventions, and imaging findings in 1,702,462 consecutive radiology reports; the 55,564 causal relations defined by the Radiology Gamuts Ontology (RGO) served as reference standard. Conditions were considered to co-occur if they were present in reports from the same patient. The φ and κ statistics both achieved AUC0.70, P<0.001 in identifying causal relationships from pairwise co-occurrence data. Analysis of radiology reports can identify a large proportion of known causal associations among diseases and imaging findings. Automated approaches hold promise to identify causal relationships among diseases and imaging findings from their co-occurrence in text-based radiology reports.",

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Causal Associations Among Diseases and Imaging Findings in Radiology Reports

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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