Implications of using artificial intelligence in the diagnosis of sepsis/sepsis shock

被引:5
作者
Gorecki, Gabriel-Petre [1 ,2 ]
Tomescu, Dana-Rodica [3 ,4 ]
Ples, Liana [5 ,6 ]
Panaitescu, Anca-Maria [5 ,7 ]
Dragosloveanu, Serban [8 ,9 ]
Scheau, Cristian [10 ,11 ]
Sima, Romina-Marina [5 ,6 ]
Coman, Ionut-Simion [12 ,13 ]
Grigorean, Valentin-Titus [12 ,13 ]
Cochior, Daniel [14 ,15 ]
机构
[1] Titu Maiorescu Univ, Fac Med, Dept Anesthesia & Intens Care, 67A Gheorghe Petrascu St, Bucharest 031593, Romania
[2] CF2 Clin Hosp, Dept Anesthesia & Intens Care, 63 Marasti Blvd, Bucharest 011464, Romania
[3] Carol Davila Univ Med & Pharm, Dept Anesthesia & Intens Care, 37 Dionisie Lupu St, Bucharest 020021, Romania
[4] Fundeni Clin Inst, Dept Anesthesia & Intens Care, 258 Fundeni Rd, Bucharest 022328, Romania
[5] Carol Davila Univ Med & Pharm, Dept Obstet & Gynecol, 37 Dionisie Lupu St, Bucharest 020021, Romania
[6] St John Emergency Clin Hosp, Dept Obstet & Gynecol, Bucur Matern 10 Intre Garle St, Bucharest 040294, Romania
[7] Filantropia Clin Hosp, Dept Obstet & Gynecol, 11-13 Ion Mihalache Blvd, Bucharest 011132, Romania
[8] Carol Davila Univ Med & Pharm, Dept Orthopaed & Traumatol, 8 Eroii Sanit Blvd, Bucharest 050474, Romania
[9] Foisor Clin Hosp Orthopaed Traumatol & Osteoarticu, Dept Orthopaed, 35-37 Ferdinand Blvd, Bucharest 021382, Romania
[10] Carol Davila Univ Med & Pharm, Dept Physiol, 8 Eroii Sanit Blvd, Bucharest 050474, Romania
[11] Foisor Clin Hosp Orthopaed Traumatol & Osteoarticu, Dept Radiol & Med Imaging, 35-37 Ferdinand Blvd, Bucharest 021382, Romania
[12] Carol Davila Univ Med & Pharm, Dept Gen Surg, 37 Dionisie Lupu St, Bucharest 020021, Romania
[13] Bagdasar Arseni Emergency Clin Hosp, Dept Gen Surg, 12 Berceni Rd, Bucharest 041915, Romania
[14] Titu Maiorescu Univ, Fac Med, Dept Gen Surg, 67A Gheorghe Petrascu St, Bucharest 031593, Romania
[15] Monza Clin Hosp, Dept Gen Surg, 27 Tony Bulandra St, Bucharest 021967, Romania
来源
GERMS | 2024年 / 14卷 / 01期
关键词
Machine learning; clinical decision support systems; biomarkers; outcome prediction; predictive modeling;
D O I
10.18683/germs.2024.1419
中图分类号
R51 [传染病];
学科分类号
100401 ;
摘要
Introduction Sepsis and septic shock represent severe pathological states, characterized by the systemic response to infection, which can lead to organ dysfunction and high mortality. Early diagnosis and rapid intervention are crucial for improving survival chances. However, the diagnosis of sepsis is complex due to its nonspecific symptoms and the variability of patient responses to infections. Methods The objective of this research was to analyze the implications of using artificial intelligence (AI) in the diagnosis of sepsis and septic shock. The research method applied in the analysis of the implications of using artificial intelligence (AI) in the diagnosis of sepsis and septic shock is the literature review. Results Among the benefits of using AI in the diagnosis of sepsis, it is noted that artificial intelligence can rapidly analyze large volumes of clinical data to identify early signs of sepsis, sometimes even before symptoms become evident to medical staff. AI models can use predictive algorithms to assess the risk of sepsis in patients, allowing for early interventions that can save lives. AI can contribute to the development of personalized treatment plans, adapting to the specific needs of each patient based on their medical history and response to treatment. The use of patient data to train AI models raises concerns regarding data privacy and security. Conclusions Artificial intelligence has the potential to revolutionize the diagnosis and treatment of sepsis, offering powerful tools for early identification and management of this critical condition. However, to realize this potential, close collaboration between researchers, clinicians, and technology developers is necessary, as well as addressing ethical and implementation challenges.
引用
收藏
页码:77 / 84
页数:8
相关论文
共 19 条
[1]  
Caraballo C, 2019, YALE J BIOL MED, V92, P629
[2]   Usefulness of ischemia-modified albumin in the diagnosis of sepsis/septic shock in the emergency department [J].
Choo, Seung Hwa ;
Lim, Yong Su ;
Cho, Jin Seong ;
Jang, Jae Ho ;
Choi, Jea Yeon ;
Choi, Woo Sung ;
Yang, Hyuk Jun .
CLINICAL AND EXPERIMENTAL EMERGENCY MEDICINE, 2020, 7 (03) :161-169
[3]  
Date P, 2024, J Autonomous Intell, V7, P1, DOI [10.32629/jai.v7i3.1084, DOI 10.32629/JAI.V7I3.1084]
[4]  
Dhungana Prabij, 2019, World J Crit Care Med, V8, P120, DOI 10.5492/wjccm.v8.i7.120
[5]   Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare [J].
Goh, Kim Huat ;
Wang, Le ;
Yeow, Adrian Yong Kwang ;
Poh, Hermione ;
Li, Ke ;
Yeow, Joannas Jie Lin ;
Tan, Gamaliel Yu Heng .
NATURE COMMUNICATIONS, 2021, 12 (01)
[6]   Early Detection of Septic Shock Onset Using Interpretable Machine Learners [J].
Misra, Debdipto ;
Avula, Venkatesh ;
Wolk, Donna M. ;
Farag, Hosam A. ;
Li, Jiang ;
Mehta, Yatin B. ;
Sandhu, Ranjeet ;
Karunakaran, Bipin ;
Kethireddy, Shravan ;
Zand, Ramin ;
Abedi, Vida .
JOURNAL OF CLINICAL MEDICINE, 2021, 10 (02) :1-17
[7]   A novel artificial intelligence based intensive care unit monitoring system: using physiological waveforms to identify sepsis [J].
Mollura, Maximiliano ;
Lehman, Li-Wei H. ;
Mark, Roger G. ;
Barbieri, Riccardo .
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, 2021, 379 (2212)
[8]  
Nagendra L., 2023, Artif Intell Cancer, V4, P1, DOI [10.35713/aic.v4.i1.1, DOI 10.35713/AIC.V4.I1.1]
[9]   Artificial Intelligence for Risk Prediction of Rehospitalization with Acute Kidney Injury in Sepsis Survivors [J].
Ou, Shuo-Ming ;
Lee, Kuo-Hua ;
Tsai, Ming-Tsun ;
Tseng, Wei-Cheng ;
Chu, Yuan-Chia ;
Tarng, Der-Cherng .
JOURNAL OF PERSONALIZED MEDICINE, 2022, 12 (01)
[10]  
Par Oznur Esra, 2022, AICCC '22: Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference, P196, DOI 10.1145/3582099.3582129