TY - GEN
T1 - Overview of CHIP 2023 Shared Task 5
T2 - Evaluation track of the 9th China Health Information Processing Conference, CHIP 2023
AU - Zong, Hui
AU - Yin, Kangping
AU - Tong, Yixuan
AU - Ma, Zhenxin
AU - Xu, Jian
AU - Tang, Buzhou
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - Evidence-based medicine has become widely adopted among medical professionals, emphasizing the need for efficient information retrieval and evidence-based clinical practice. The PICOS framework, which stands for Population, Intervention, Comparison, Outcome, and Study design, has gained popularity as a structured approach for formulating research questions and retrieving relevant literature. In this paper, we present an overview of the “Medical Literature PICOS Identification” task organized at the CHIP 2023. The task aims to develop automated systems that accurately extract key information related to the PICOS components from Chinese medical research papers. The dataset consists of 4500 academic papers, divided into training, development, and test sets. Macro-F1 score are used as evaluation metric. Thirteen participating teams submitted their solutions, with the top-ranked team achieving a score of 0.81. This paper provides valuable insights into the challenges, methodologies, and performance of the top-ranked teams, serving as a resource for future research and system development in this domain.
AB - Evidence-based medicine has become widely adopted among medical professionals, emphasizing the need for efficient information retrieval and evidence-based clinical practice. The PICOS framework, which stands for Population, Intervention, Comparison, Outcome, and Study design, has gained popularity as a structured approach for formulating research questions and retrieving relevant literature. In this paper, we present an overview of the “Medical Literature PICOS Identification” task organized at the CHIP 2023. The task aims to develop automated systems that accurately extract key information related to the PICOS components from Chinese medical research papers. The dataset consists of 4500 academic papers, divided into training, development, and test sets. Macro-F1 score are used as evaluation metric. Thirteen participating teams submitted their solutions, with the top-ranked team achieving a score of 0.81. This paper provides valuable insights into the challenges, methodologies, and performance of the top-ranked teams, serving as a resource for future research and system development in this domain.
KW - CHIP
KW - Information extraction
KW - Large language model
KW - PICOS
UR - https://www.scopus.com/pages/publications/85189613694
U2 - 10.1007/978-981-97-1717-0_14
DO - 10.1007/978-981-97-1717-0_14
M3 - 会议稿件
AN - SCOPUS:85189613694
SN - 9789819717163
T3 - Communications in Computer and Information Science
SP - 159
EP - 165
BT - Health Information Processing. Evaluation Track Papers - 9th China Conference, CHIP 2023, Proceedings
A2 - Xu, Hua
A2 - Chen, Qingcai
A2 - Lin, Hongfei
A2 - Wu, Fei
A2 - Liu, Lei
A2 - Tang, Buzhou
A2 - Hao, Tianyong
A2 - Huang, Zhengxing
A2 - Lei, Jianbo
A2 - Li, Zuofeng
A2 - Zong, Hui
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 27 October 2023 through 29 October 2023
ER -