Data-Driven Modeling of Randomized Controlled Trial Outcomes

Zhehuan Chen; Yilu Fang; Hao Liu; Chunhua Weng

doi:10.3233/SHTI220481

Data-Driven Modeling of Randomized Controlled Trial Outcomes

Zhehuan Chen
, Yilu Fang
, Hao Liu
, Chunhua Weng

Computer Science

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

1 Scopus citations

Abstract

Anecdotally, 38.5% of clinical outcome descriptions in randomized controlled trial publications contain complex text. Existing terminologies are insufficient to standardize outcomes and their measures, temporal attributes, quantitative metrics, and other attributes. In this study, we analyzed the semantic patterns in the outcome text in a sample of COVID-19 trials and presented a data-driven method for modeling outcomes. We conclude that a data-driven knowledge representation can benefit natural language processing of outcome text from published clinical studies.

Original language	English
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	392-396
Number of pages	5
ISBN (Electronic)	9781643682846
DOIs	https://doi.org/10.3233/SHTI220481
State	Published - 25 May 2022
Event	32nd Medical Informatics Europe Conference, MIE 2022 - Nice, France Duration: 27 May 2022 → 30 May 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	27/05/22 → 30/05/22

Keywords

knowledge representation
outcome
randomized controlled trials

Access to Document

10.3233/SHTI220481

Cite this

Chen, Z., Fang, Y., Liu, H., & Weng, C. (2022). Data-Driven Modeling of Randomized Controlled Trial Outcomes. 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. 392-396). (Studies in Health Technology and Informatics; Vol. 294). IOS Press BV. https://doi.org/10.3233/SHTI220481

Chen, Zhehuan ; Fang, Yilu ; Liu, Hao et al. / Data-Driven Modeling of Randomized Controlled Trial Outcomes. 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. 392-396 (Studies in Health Technology and Informatics).

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title = "Data-Driven Modeling of Randomized Controlled Trial Outcomes",

abstract = "Anecdotally, 38.5\% of clinical outcome descriptions in randomized controlled trial publications contain complex text. Existing terminologies are insufficient to standardize outcomes and their measures, temporal attributes, quantitative metrics, and other attributes. In this study, we analyzed the semantic patterns in the outcome text in a sample of COVID-19 trials and presented a data-driven method for modeling outcomes. We conclude that a data-driven knowledge representation can benefit natural language processing of outcome text from published clinical studies.",

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author = "Zhehuan Chen and Yilu Fang and Hao Liu and Chunhua Weng",

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year = "2022",

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doi = "10.3233/SHTI220481",

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editor = "Brigitte Seroussi and Patrick Weber and Ferdinand Dhombres and Cyril Grouin and Jan-David Liebe and Jan-David Liebe and Jan-David Liebe and Sylvia Pelayo and Andrea Pinna and Bastien Rance and Bastien Rance and Lucia Sacchi and Adrien Ugon and Adrien Ugon and Arriel Benis and Parisis Gallos",

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Chen, Z, Fang, Y, Liu, H & Weng, C 2022, Data-Driven Modeling of Randomized Controlled Trial Outcomes. 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. 392-396, 32nd Medical Informatics Europe Conference, MIE 2022, Nice, France, 27/05/22. https://doi.org/10.3233/SHTI220481

Data-Driven Modeling of Randomized Controlled Trial Outcomes. / Chen, Zhehuan; Fang, Yilu; Liu, Hao et al.
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. 392-396 (Studies in Health Technology and Informatics; Vol. 294).

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

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N2 - Anecdotally, 38.5% of clinical outcome descriptions in randomized controlled trial publications contain complex text. Existing terminologies are insufficient to standardize outcomes and their measures, temporal attributes, quantitative metrics, and other attributes. In this study, we analyzed the semantic patterns in the outcome text in a sample of COVID-19 trials and presented a data-driven method for modeling outcomes. We conclude that a data-driven knowledge representation can benefit natural language processing of outcome text from published clinical studies.

AB - Anecdotally, 38.5% of clinical outcome descriptions in randomized controlled trial publications contain complex text. Existing terminologies are insufficient to standardize outcomes and their measures, temporal attributes, quantitative metrics, and other attributes. In this study, we analyzed the semantic patterns in the outcome text in a sample of COVID-19 trials and presented a data-driven method for modeling outcomes. We conclude that a data-driven knowledge representation can benefit natural language processing of outcome text from published clinical studies.

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Chen Z, Fang Y, Liu H, Weng C. Data-Driven Modeling of Randomized Controlled Trial Outcomes. 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. 392-396. (Studies in Health Technology and Informatics). doi: 10.3233/SHTI220481

Data-Driven Modeling of Randomized Controlled Trial Outcomes

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