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Analytics with artificial intelligence to advance the treatment of acute respiratory distress syndrome
Author(s) -
Zhang Zhongheng,
Navarese Eliano Pio,
Zheng Bin,
Meng Qinghe,
Liu Nan,
Ge Huiqing,
Pan Qing,
Yu Yuetian,
Ma Xuelei
Publication year - 2020
Publication title -
journal of evidence‐based medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.885
H-Index - 22
ISSN - 1756-5391
DOI - 10.1111/jebm.12418
Subject(s) - ards , analytics , medicine , population , artificial intelligence , big data , identification (biology) , computer science , intensive care medicine , machine learning , data science , data mining , lung , botany , environmental health , biology
Artificial intelligence (AI) has found its way into clinical studies in the era of big data. Acute respiratory distress syndrome (ARDS) or acute lung injury (ALI) is a clinical syndrome that encompasses a heterogeneous population. Management of such heterogeneous patient population is a big challenge for clinicians. With accumulating ALI datasets being publicly available, more knowledge could be discovered with sophisticated analytics. We reviewed literatures with big data analytics to understand the role of AI for improving the caring of patients with ALI/ARDS. Many studies have utilized the electronic medical records (EMR) data for the identification and prognostication of ARDS patients. As increasing number of ARDS clinical trials data is open to public, secondary analysis on these combined datasets provide a powerful way of finding solution to clinical questions with a new perspective. AI techniques such as Classification and Regression Tree (CART) and artificial neural networks (ANN) have also been successfully used in the investigation of ARDS problems. Individualized treatment of ARDS could be implemented with a support from AI as we are now able to classify ARDS into many subphenotypes by unsupervised machine learning algorithms. Interestingly, these subphenotypes show different responses to a certain intervention. However, current analytics involving ARDS have not fully incorporated information from omics such as transcriptome, proteomics, daily activities and environmental conditions. AI technology is assisting us to interpret complex data of ARDS patients and enable us to further improve the management of ARDS patients in future with individual treatment plans.