Intersections between artificial intelligence (AI) and sepsis: an integrative review
DOI:
https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1268Keywords:
Algorithms, Machine Learning, SepsisAbstract
Objectives: To conduct an integrative literature review to investigate the impact of artificial intelligence (AI) on the clinical management of sepsis. Methods: Databases such as PubMed/MEDLINE and LILACS were utilized, and the search for articles was guided by the question: what is the contribution of AI to the detection and/or treatment of sepsis? Results: Of the 11 selected articles, the fundamental role of Machine Learning in developing predictive models for early sepsis detection was highlighted, resulting in improvements in interventions and prognoses. Additionally, AI was applied in patient monitoring systems, such as the Robô Laura™, optimizing clinical processes. Conclusions: AI plays a significant role in advancing the clinical management of sepsis, offering innovative perspectives for diagnosis, treatment, and prognosis.
References
Wu M, Gu R, Wei, Ji. Artificial intelligence for clinical decision support in sepsis. Frontiers in Medicine. 2021 May; 8(6654640): 1-9. doi:10.3389/fmed.2021.665464
Taniguchi LU, Bierrenbach AL, Toscano CM, Schettino GPP, Azevedo LCP. Sepsis-related deaths in Brazil: an analysis of the national mortality registry from 2002 to 2010. Crit Care. 2014;18(6):608.doi:10.1186/s13054-014-0608-8
Fleischman C, Scherag A, Adhikari NK, Hartog CS, Tsaganos T, Schlattmann P, et al. Current estimates and limitations assessment of global incidence and mortality of hospitaltreated sepsis.. Am J Respir Crit Care Med. 2016; 1; 193(3):259-72.doi: 10.1164/rccm.201504-0781OC
Gonçalves LS, Amaro ML de M, Romero A de LM, Schamne FK, Fressatto JL, Bezerra CW. Implementation of an Artificial Intelligence Algorithm for sepsis detection. Rev Bras Enferm. 2020; 73(3):1-5.doi:10.1590/0034-7167-2018-0421
McCarthy J, Minsky M, Rochester N, Shannon C. A proposal for the Dartmouth summer research project on artificial intelligence. AI Magazine. 2006; 27(4):12. doi:10.1609/aimag. v27i4.1904
Greco M, Caruso PF, Cecconi M. Artificial intelligence in the intensive care unit. Semin Resp Crit Care. 2021; 42:2–9. doi: 10.1055/s-0040-1719037
Soares CR, Peres HHC, de Oliveira NB. Processo de Enfermagem: revisão integrativa sobre as contribuições da informática. J Health Inform. 2018;10(4):113-118.
Kalil AJ, Dias VM de CH, Rocha C da C, Morales HMP, Fressatto JL, Faria RA de. Sepsis risk assessment: a retrospective analysis after a cognitive risk management robot (Robot Laura®) implementation in a clinical-surgical unit. Res Biomed Eng. 2018;34(4):310–316. doi.org/10.1590/2446-4740.180021
van Doorn WPTM, Stassen PM, Borggreve HF, Schalkwijk MJ, Stoffers J, Bekers O, et al. A comparison of machine learning models versus clinical evaluation for mortality prediction in patients with sepsis. PLoS ONE. 2021; 16(1): 1-15. https://doi.org/10.1371/journal.
pone.0245157
Kudo D, Goto T, Uchimido R, Hayakawa M, Yamakawa K, Abe T, Shiraishi A, Kushimoto S. Coagulation phenotypes in sepsis and effects of recombinant human thrombomodulin: an analysis of three multicentre observational studies. Crit Care. 2021; 25(1):1-11. doi: 10.1186/s13054-021-03541-5.
Scherer JS, Pereira JS, Debastiani MS, Bica CG. Beyond technology: Can artificial intelligence support clinical decisions in the prediction of sepsis? Rev Bras Enferm. 2022; 75(5):1-6. doi: 10.1590/0034-7167-2021-0586.
Wang D, Li J, Sun Y, Ding X, Zhang X, Liu S, Han B, Wang H, Duan X, Sun T. A Machine Learning Model for Accurate Prediction of Sepsis in ICU Patients. Front Public Health. 2021; 9:754348. doi: 10.3389/fpubh.2021.754348.
Hong X, Liu G, Chi Z, Yang T, Zhang Y. Predictive model for urosepsis in patients with Upper Urinary Tract Calculi based on ultrasonography and urinalysis using artificial intelligence learning. Int Braz J Urol. 2023;49(2):221–32. doi.org/10.1590/S1677-5538.
Li Y, Wu Y, Gao Y, Niu X, Li J, Tang M, Fu C, Qi R, Song B, Chen H, Gao X, Yang Y, Guan X. Machine-learning based prediction of prognostic risk factors in patients with invasive candidiasis infection and bacterial bloodstream infection: a singled centered retrospective study. BMC Infect Dis. 2022; 22(1):1-11. doi: 10.1186/s12879-022-07125-8.
Liaw SY, Tan JZ, Bin Rusli KD, Ratan R, Zhou W, Lim S, Lau TC, Seah B, Chua WL. Artificial Intelligence Versus Human-Controlled Doctor in Virtual Reality Simulation for Sepsis Team Training: Randomized Controlled Study. J Med Internet Res. 2023;25: 1-9. doi: 10.2196/47748.
Liu F, Yao J, Liu C, Shou S. Construction and validation of machine learning models for sepsis prediction in patients with acute pancreatitis. BMC Surg. 2023; 23(1): 1-13. doi: 10.1186/s12893-023-02151-y.
Pan X, Xie J, Zhang L, Wang X, Zhang S, Zhuang Y, Lin X, Shi S, Shi S, Lin W. Evaluate prognostic accuracy of SOFA component score for mortality among adults with sepsis by machine learning method. BMC Infect Dis. 2023; 23(1): 1-8. doi: 10.1186/s12879-023-08045-x.
She H, Du Y, Du Y, Tan L, Yang S, Luo X, Li Q, Xiang X, Lu H, Hu Y, Liu L, Li T. Metabolomics and machine learning approaches for diagnostic and prognostic biomarkers screening in sepsis. BMC Anesthesiol. 2023;23(1): 1-13. doi: 10.1186/s12871-023-02317-4.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Submission of a paper to Journal of Health Informatics is understood to imply that it is not being considered for publication elsewhere and that the author(s) permission to publish his/her (their) article(s) in this Journal implies the exclusive authorization of the publishers to deal with all issues concerning the copyright therein. Upon the submission of an article, authors will be asked to sign a Copyright Notice. Acceptance of the agreement will ensure the widest possible dissemination of information. An e-mail will be sent to the corresponding author confirming receipt of the manuscript and acceptance of the agreement.
