Assessment of medical reports uncertainty through topic modeling and machine learning

Mengyuan Shang; Jacob Furst; Daniela Stan-Raicu

doi:10.1109/CBMS49503.2020.00043

Assessment of medical reports uncertainty through topic modeling and machine learning

Mengyuan Shang
, Jacob Furst
, Daniela Stan-Raicu

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

Original language	English
Title of host publication	Proceedings - 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems, CBMS 2020
Editors	Alba Garcia Seco de Herrera, Alejandro Rodriguez Gonzalez, KC Santosh, Zelalem Temesgen, Bridget Kane, Paolo Soda
Publisher	IEEE (Institute of Electrical and Electronics Engineers)
Pages	189-192
Number of pages	4
ISBN (Electronic)	9781728194295
DOIs	https://doi.org/10.1109/CBMS49503.2020.00043
State	Published - Jul 2020
Event	33rd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2020 - Virtual, Online, United States Duration: Jul 28 2020 → Jul 30 2020

Publication series

Name	Proceedings - IEEE Symposium on Computer-Based Medical Systems
Volume	2020-July
ISSN (Print)	1063-7125

Conference

Conference	33rd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2020
Country/Territory	United States
City	Virtual, Online
Period	7/28/20 → 7/30/20

ASJC Scopus Subject Areas

Radiology Nuclear Medicine and imaging
Computer Science Applications

Access to Document

10.1109/CBMS49503.2020.00043

Cite this

Shang, M., Furst, J., & Stan-Raicu, D. (2020). Assessment of medical reports uncertainty through topic modeling and machine learning. In A. G. S. de Herrera, A. Rodriguez Gonzalez, KC. Santosh, Z. Temesgen, B. Kane, & P. Soda (Eds.), Proceedings - 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems, CBMS 2020 (pp. 189-192). Article 9183233 (Proceedings - IEEE Symposium on Computer-Based Medical Systems; Vol. 2020-July). IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/CBMS49503.2020.00043

Assessment of medical reports uncertainty through topic modeling and machine learning. / Shang, Mengyuan; Furst, Jacob; Stan-Raicu, Daniela.
Proceedings - 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems, CBMS 2020. ed. / Alba Garcia Seco de Herrera; Alejandro Rodriguez Gonzalez; KC Santosh; Zelalem Temesgen; Bridget Kane; Paolo Soda. IEEE (Institute of Electrical and Electronics Engineers), 2020. p. 189-192 9183233 (Proceedings - IEEE Symposium on Computer-Based Medical Systems; Vol. 2020-July).

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

Shang, M, Furst, J & Stan-Raicu, D 2020, Assessment of medical reports uncertainty through topic modeling and machine learning. in AGS de Herrera, A Rodriguez Gonzalez, KC Santosh, Z Temesgen, B Kane & P Soda (eds), Proceedings - 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems, CBMS 2020., 9183233, Proceedings - IEEE Symposium on Computer-Based Medical Systems, vol. 2020-July, IEEE (Institute of Electrical and Electronics Engineers), pp. 189-192, 33rd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2020, Virtual, Online, United States, 7/28/20. https://doi.org/10.1109/CBMS49503.2020.00043

Shang M, Furst J, Stan-Raicu D. Assessment of medical reports uncertainty through topic modeling and machine learning. In de Herrera AGS, Rodriguez Gonzalez A, Santosh KC, Temesgen Z, Kane B, Soda P, editors, Proceedings - 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems, CBMS 2020. IEEE (Institute of Electrical and Electronics Engineers). 2020. p. 189-192. 9183233. (Proceedings - IEEE Symposium on Computer-Based Medical Systems). doi: 10.1109/CBMS49503.2020.00043

Shang, Mengyuan ; Furst, Jacob ; Stan-Raicu, Daniela. / Assessment of medical reports uncertainty through topic modeling and machine learning. Proceedings - 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems, CBMS 2020. editor / Alba Garcia Seco de Herrera ; Alejandro Rodriguez Gonzalez ; KC Santosh ; Zelalem Temesgen ; Bridget Kane ; Paolo Soda. IEEE (Institute of Electrical and Electronics Engineers), 2020. pp. 189-192 (Proceedings - IEEE Symposium on Computer-Based Medical Systems).

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Assessment of medical reports uncertainty through topic modeling and machine learning

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ASJC Scopus Subject Areas

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