Luo MH, Huang DL, Luo JC, Su Y, Li JK, Tu GW, Luo Z. Data science in the intensive care unit. World J Crit Care Med 2022; 11(5): 311-316 [PMID: 36160936 DOI: 10.5492/wjccm.v11.i5.311]
Corresponding Author of This Article
Zhe Luo, MD, PhD, Chief Doctor, Department of Critical Care Medicine, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Shanghai 200032, China. luo.zhe@zs-hospital.sh.cn
Research Domain of This Article
Critical Care Medicine
Article-Type of This Article
Editorial
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Crit Care Med. Sep 9, 2022; 11(5): 311-316 Published online Sep 9, 2022. doi: 10.5492/wjccm.v11.i5.311
Data science in the intensive care unit
Ming-Hao Luo, Dan-Lei Huang, Jing-Chao Luo, Ying Su, Jia-Kun Li, Guo-Wei Tu, Zhe Luo
Ming-Hao Luo, Dan-Lei Huang, Shanghai Medical College, Fudan University, Shanghai 200032, China
Jing-Chao Luo, Ying Su, Jia-Kun Li, Guo-Wei Tu, Zhe Luo, Department of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China
Author contributions: Luo MH drafted the manuscript; Tu GW and Luo Z substantively revised it; All authors participated in the conception and design of the work.
Conflict-of-interest statement: All authors have no conflicts of interest to declare.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Zhe Luo, MD, PhD, Chief Doctor, Department of Critical Care Medicine, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Shanghai 200032, China. luo.zhe@zs-hospital.sh.cn
Received: April 11, 2022 Peer-review started: April 11, 2022 First decision: April 28, 2022 Revised: May 3, 2022 Accepted: July 16, 2022 Article in press: July 16, 2022 Published online: September 9, 2022
Abstract
In this editorial, we comment on the current development and deployment of data science in intensive care units (ICUs). Data in ICUs can be classified into qualitative and quantitative data with different technologies needed to translate and interpret them. Data science, in the form of artificial intelligence (AI), should find the right interaction between physicians, data and algorithm. For individual patients and physicians, sepsis and mechanical ventilation have been two important aspects where AI has been extensively studied. However, major risks of bias, lack of generalizability and poor clinical values remain. AI deployment in the ICUs should be emphasized more to facilitate AI development. For ICU management, AI has a huge potential in transforming resource allocation. The coronavirus disease 2019 pandemic has given opportunities to establish such systems which should be investigated further. Ethical concerns must be addressed when designing such AI.
Core Tip: Data in intensive care units (ICUs) can be classified into qualitative and quantitative data with different technologies needed to translate and interpret them. Data science, in the form of artificial intelligence (AI), should find the right interaction between physicians, data and algorithm to maximize the utility. AI deployment in the ICUs should be emphasized more to facilitate AI development. Individual-level applications such as disease prediction, and ICU-level potentials such as resource allocation are both of paramount importance.
