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Editorial
. 2022 Sep 9;11(5):311-316.
doi: 10.5492/wjccm.v11.i5.311.

Data science in the intensive care unit

Ming-Hao Luo  1 , Dan-Lei Huang  1 , Jing-Chao Luo  2 , Ying Su  2 , Jia-Kun Li  2 , Guo-Wei Tu  2 , Zhe Luo  3
Affiliations
Editorial

Data science in the intensive care unit

Ming-Hao Luo et al. World J Crit Care Med. .

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.

Keywords: Artificial intelligence; COVID-19; Data science; Intensive care units; Interaction.

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Conflict of interest statement

Conflict-of-interest statement: All authors have no conflicts of interest to declare.

Figures

Figure 1
Figure 1
Interaction between artificial intelligence development and artificial intelligence deployment. Artificial intelligence (AI) development and AI deployment should be combined to revise current models and offer tangible benefits derived from current researches. AI development should find the right interaction between three roles: physicians, data and algorithm. AI deployment in the form of prospective randomized controlled trials can facilitate published models to generate bedside merits and evaluate whether major biases exist. The results from deployment testing can, in turn, offer insights into the development and modify the substandard algorithm. CRRT: Continuous renal replacement therapy; ECMO: Extracorporeal membrane oxygenation.
Figure 2
Figure 2
Resource allocation in the intensive care units. The applications of machine learning can target patients in need of intensive care units (ICUs) and predict the use of ICU resources. Machine learning can predict ICU transfer in hospitalized patients and predict the use of ICU resources, such as mechanical ventilation. It gives the chance to make the most use of resources, especially in ICUs where demand and supply frequently mismatch. Prediction in interventions, such as mechanical ventilation, would mean that the management groups can foresee changes and mobilize resource, such as equipment and staff, to cope with such demands in advance which is a positive factor for patient outcomes. AI: Artificial intelligence.
See this image and copyright information in PMC

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