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Chinese experts’ consensus on the application of intensive care big data

Authors :
Longxiang Su
Shengjun Liu
Yun Long
Chaodong Chen
Kai Chen
Ming Chen
Yaolong Chen
Yisong Cheng
Yating Cui
Qi Ding
Renyu Ding
Meili Duan
Tao Gao
Xiaohua Gu
Hongli He
Jiawei He
Bo Hu
Chang Hu
Rui Huang
Xiaobo Huang
Huizhen Jiang
Jing Jiang
Yunping Lan
Jun Li
Linfeng Li
Lu Li
Wenxiong Li
Yongzai Li
Jin Lin
Xufei Luo
Feng Lyu
Zhi Mao
He Miao
Xiaopu Shang
Xiuling Shang
You Shang
Yuwen Shen
Yinghuan Shi
Qihang Sun
Weijun Sun
Zhiyun Tang
Bo Wang
Haijun Wang
Hongliang Wang
Li Wang
Luhao Wang
Sicong Wang
Zhanwen Wang
Zhong Wang
Dong Wei
Jianfeng Wu
Qin Wu
Xuezhong Xing
Jin Yang
Xianghong Yang
Jiangquan Yu
Wenkui Yu
Yuan Yu
Hao Yuan
Qian Zhai
Hao Zhang
Lina Zhang
Meng Zhang
Zhongheng Zhang
Chunguang Zhao
Ruiqiang Zheng
Lei Zhong
Feihu Zhou
Weiguo Zhu
Source :
Frontiers in Medicine, Vol 10 (2024)
Publication Year :
2024
Publisher :
Frontiers Media S.A., 2024.

Abstract

The development of intensive care medicine is inseparable from the diversified monitoring data. Intensive care medicine has been closely integrated with data since its birth. Critical care research requires an integrative approach that embraces the complexity of critical illness and the computational technology and algorithms that can make it possible. Considering the need of standardization of application of big data in intensive care, Intensive Care Medicine Branch of China Health Information and Health Care Big Data Society, Standard Committee has convened expert group, secretary group and the external audit expert group to formulate Chinese Experts’ Consensus on the Application of Intensive Care Big Data (2022). This consensus makes 29 recommendations on the following five parts: Concept of intensive care big data, Important scientific issues, Standards and principles of database, Methodology in solving big data problems, Clinical application and safety consideration of intensive care big data. The consensus group believes this consensus is the starting step of application big data in the field of intensive care. More explorations and big data based retrospective research should be carried out in order to enhance safety and reliability of big data based models of critical care field.

Details

Language :
English
ISSN :
2296858X
Volume :
10
Database :
Directory of Open Access Journals
Journal :
Frontiers in Medicine
Publication Type :
Academic Journal
Accession number :
edsdoj.8206b98856c4b4c855ea59bbc9fbb30
Document Type :
article
Full Text :
https://doi.org/10.3389/fmed.2023.1174429