Extracting Decision Trees from Medical Texts: An Overview of the Text2DT Track in CHIP2022

Wei Zhu; Wenfeng Li; Xiaoling Wang; Wendi Ji; Yuanbin Wu; Jin Chen; Liang Chen; Buzhou Tang

doi:10.1007/978-981-99-4826-0_9

Extracting Decision Trees from Medical Texts: An Overview of the Text2DT Track in CHIP2022

Wei Zhu, Wenfeng Li, Xiaoling Wang^*, Wendi Ji, Yuanbin Wu, Jin Chen, Liang Chen, Buzhou Tang

^*Corresponding author for this work

School of Computer Science and Technology

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

3 Scopus citations

Abstract

This paper presents an overview of the Text2DT shared task ¹ held in the CHIP-2022 shared tasks. The shared task addresses the challenging topic of automatically extracting the medical decision trees from the un-structured medical texts such as medical guidelines and textbooks. Many teams from both industry and academia participated in the shared tasks, and the top teams achieved amazing test results. This paper describes the tasks, the datasets, evaluation metrics, and the top systems for both tasks. Finally, the paper summarizes the techniques and results of the evaluation of the various approaches explored by the participating teams. ¹ (http://cips-chip.org.cn/2022/eval3

Original language	English
Title of host publication	Health Information Processing. Evaluation Track Papers - 8th China Conference, CHIP 2022, Revised Selected Papers
Editors	Buzhou Tang, Qingcai Chen, Hongfei Lin, Fei Wu, Lei Liu, Tianyong Hao, Yanshan Wang, Haitian Wang, Jianbo Lei, Zuofeng Li, Hui Zong
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	89-102
Number of pages	14
ISBN (Print)	9789819948253
DOIs	https://doi.org/10.1007/978-981-99-4826-0_9
State	Published - 2023
Event	Proceedings of the 8th China Conference on China Health Information Processing Conference 2022 - Hangzhou, China Duration: 21 Oct 2022 → 23 Oct 2022

Publication series

Name	Communications in Computer and Information Science
Volume	1773 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	Proceedings of the 8th China Conference on China Health Information Processing Conference 2022
Country/Territory	China
City	Hangzhou
Period	21/10/22 → 23/10/22

Keywords

Information extraction
Pretrained models
Text2DT

Access to Document

10.1007/978-981-99-4826-0_9

Cite this

Zhu, W., Li, W., Wang, X., Ji, W., Wu, Y., Chen, J., Chen, L., & Tang, B. (2023). Extracting Decision Trees from Medical Texts: An Overview of the Text2DT Track in CHIP2022. In B. Tang, Q. Chen, H. Lin, F. Wu, L. Liu, T. Hao, Y. Wang, H. Wang, J. Lei, Z. Li, & H. Zong (Eds.), Health Information Processing. Evaluation Track Papers - 8th China Conference, CHIP 2022, Revised Selected Papers (pp. 89-102). (Communications in Computer and Information Science; Vol. 1773 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-99-4826-0_9

Zhu, Wei ; Li, Wenfeng ; Wang, Xiaoling et al. / Extracting Decision Trees from Medical Texts : An Overview of the Text2DT Track in CHIP2022. Health Information Processing. Evaluation Track Papers - 8th China Conference, CHIP 2022, Revised Selected Papers. editor / Buzhou Tang ; Qingcai Chen ; Hongfei Lin ; Fei Wu ; Lei Liu ; Tianyong Hao ; Yanshan Wang ; Haitian Wang ; Jianbo Lei ; Zuofeng Li ; Hui Zong. Springer Science and Business Media Deutschland GmbH, 2023. pp. 89-102 (Communications in Computer and Information Science).

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title = "Extracting Decision Trees from Medical Texts: An Overview of the Text2DT Track in CHIP2022",

abstract = "This paper presents an overview of the Text2DT shared task 1 held in the CHIP-2022 shared tasks. The shared task addresses the challenging topic of automatically extracting the medical decision trees from the un-structured medical texts such as medical guidelines and textbooks. Many teams from both industry and academia participated in the shared tasks, and the top teams achieved amazing test results. This paper describes the tasks, the datasets, evaluation metrics, and the top systems for both tasks. Finally, the paper summarizes the techniques and results of the evaluation of the various approaches explored by the participating teams. 1 (http://cips-chip.org.cn/2022/eval3",

keywords = "Information extraction, Pretrained models, Text2DT",

author = "Wei Zhu and Wenfeng Li and Xiaoling Wang and Wendi Ji and Yuanbin Wu and Jin Chen and Liang Chen and Buzhou Tang",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.; Proceedings of the 8th China Conference on China Health Information Processing Conference 2022 ; Conference date: 21-10-2022 Through 23-10-2022",

year = "2023",

doi = "10.1007/978-981-99-4826-0\_9",

language = "英语",

isbn = "9789819948253",

series = "Communications in Computer and Information Science",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "89--102",

editor = "Buzhou Tang and Qingcai Chen and Hongfei Lin and Fei Wu and Lei Liu and Tianyong Hao and Yanshan Wang and Haitian Wang and Jianbo Lei and Zuofeng Li and Hui Zong",

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Zhu, W, Li, W, Wang, X, Ji, W, Wu, Y, Chen, J, Chen, L & Tang, B 2023, Extracting Decision Trees from Medical Texts: An Overview of the Text2DT Track in CHIP2022. in B Tang, Q Chen, H Lin, F Wu, L Liu, T Hao, Y Wang, H Wang, J Lei, Z Li & H Zong (eds), Health Information Processing. Evaluation Track Papers - 8th China Conference, CHIP 2022, Revised Selected Papers. Communications in Computer and Information Science, vol. 1773 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 89-102, Proceedings of the 8th China Conference on China Health Information Processing Conference 2022, Hangzhou, China, 21/10/22. https://doi.org/10.1007/978-981-99-4826-0_9

Extracting Decision Trees from Medical Texts: An Overview of the Text2DT Track in CHIP2022. / Zhu, Wei; Li, Wenfeng; Wang, Xiaoling et al.
Health Information Processing. Evaluation Track Papers - 8th China Conference, CHIP 2022, Revised Selected Papers. ed. / Buzhou Tang; Qingcai Chen; Hongfei Lin; Fei Wu; Lei Liu; Tianyong Hao; Yanshan Wang; Haitian Wang; Jianbo Lei; Zuofeng Li; Hui Zong. Springer Science and Business Media Deutschland GmbH, 2023. p. 89-102 (Communications in Computer and Information Science; Vol. 1773 CCIS).

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

TY - GEN

T1 - Extracting Decision Trees from Medical Texts

T2 - Proceedings of the 8th China Conference on China Health Information Processing Conference 2022

AU - Zhu, Wei

AU - Li, Wenfeng

AU - Wang, Xiaoling

AU - Ji, Wendi

AU - Wu, Yuanbin

AU - Chen, Jin

AU - Chen, Liang

AU - Tang, Buzhou

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

PY - 2023

Y1 - 2023

N2 - This paper presents an overview of the Text2DT shared task 1 held in the CHIP-2022 shared tasks. The shared task addresses the challenging topic of automatically extracting the medical decision trees from the un-structured medical texts such as medical guidelines and textbooks. Many teams from both industry and academia participated in the shared tasks, and the top teams achieved amazing test results. This paper describes the tasks, the datasets, evaluation metrics, and the top systems for both tasks. Finally, the paper summarizes the techniques and results of the evaluation of the various approaches explored by the participating teams. 1 (http://cips-chip.org.cn/2022/eval3

AB - This paper presents an overview of the Text2DT shared task 1 held in the CHIP-2022 shared tasks. The shared task addresses the challenging topic of automatically extracting the medical decision trees from the un-structured medical texts such as medical guidelines and textbooks. Many teams from both industry and academia participated in the shared tasks, and the top teams achieved amazing test results. This paper describes the tasks, the datasets, evaluation metrics, and the top systems for both tasks. Finally, the paper summarizes the techniques and results of the evaluation of the various approaches explored by the participating teams. 1 (http://cips-chip.org.cn/2022/eval3

KW - Information extraction

KW - Pretrained models

KW - Text2DT

UR - https://www.scopus.com/pages/publications/85172721087

U2 - 10.1007/978-981-99-4826-0_9

DO - 10.1007/978-981-99-4826-0_9

M3 - 会议稿件

AN - SCOPUS:85172721087

SN - 9789819948253

T3 - Communications in Computer and Information Science

SP - 89

EP - 102

BT - Health Information Processing. Evaluation Track Papers - 8th China Conference, CHIP 2022, Revised Selected Papers

A2 - Tang, Buzhou

A2 - Chen, Qingcai

A2 - Lin, Hongfei

A2 - Wu, Fei

A2 - Liu, Lei

A2 - Hao, Tianyong

A2 - Wang, Yanshan

A2 - Wang, Haitian

A2 - Lei, Jianbo

A2 - Li, Zuofeng

A2 - Zong, Hui

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 21 October 2022 through 23 October 2022

ER -

Zhu W, Li W, Wang X, Ji W, Wu Y, Chen J et al. Extracting Decision Trees from Medical Texts: An Overview of the Text2DT Track in CHIP2022. In Tang B, Chen Q, Lin H, Wu F, Liu L, Hao T, Wang Y, Wang H, Lei J, Li Z, Zong H, editors, Health Information Processing. Evaluation Track Papers - 8th China Conference, CHIP 2022, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2023. p. 89-102. (Communications in Computer and Information Science). doi: 10.1007/978-981-99-4826-0_9