The application of artificial intelligence technology in intensive care medicine in the last ten years: a visualization analysis_Chinese Journal of Evidence-Based Medicine

Authors：

ZHOU Hao ^1,2 , ZHANG Yongjia ^1,2 ,  XU Ying ^1,2

1. Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;
2. West China Hospital School of Nursing, Sichuan University, Chengdu 610041, P. R. China;

Corresponding author：

XU Ying, Email: 1419850448@qq.com

Keywords：

Visualization analysis; Critical care medicine; Artificial intelligence; Clustering analysis

DOI：

10.7507/1672-2531.202303058

Video：

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Abstract Full text Figures/Tables Video References Cited by

Objective To analyze the hot spot and future application trend of artificial intelligence technology in the field of intensive care medicine. Methods The CNKI, WanFang Data, VIP and Web of Science core collection databases were electronically searched to collect the related literature about the application of artificial intelligence in the field of critical medicine from January 1, 2013 to December 31, 2022. Bibliometrics was used to visually analyze the author, country, research institution, co-cited literature and key words. Results A total of 986 Chinese articles and 4 016 English articles were included. The number of articles published had increased year by year in the past decade, and the top three countries in English literature were China, the United States and Germany. The predictive model and machine learning were the most frequent key words in Chinese and English literature, respectively. Predicting disease progression, mortality and prognosis were the research focus of artificial intelligence in the field of critical medicine. Conclusion The application of artificial intelligence in the field of critical medicine is on the rise, and the research hotspots are mainly related to monitoring, predicting disease progression, mortality, disease prognosis and the classification of disease phenotypes or subtypes.

Citation： ZHOU Hao, ZHANG Yongjia, XU Ying. The application of artificial intelligence technology in intensive care medicine in the last ten years: a visualization analysis. Chinese Journal of Evidence-Based Medicine, 2023, 23(8): 930-935. doi: 10.7507/1672-2531.202303058 Copy

1.	Kulkarni S, Seneviratne N, Baig MS, et al. Artificial intelligence in medicine: where are we now. Acad Radiol, 2020, 27(1): 62-70.
2.	Hassoun S, Jefferson F, Shi X, et al. Artificial intelligence for biology. Integr Comp Biol, 2022, 61(6): 2267-2275.
3.	Xue Y, Chen C, Tan R, et al. Artificial intelligence-assisted bioinformatics, microneedle, and diabetic wound healing: a "new deal" of an old drug. ACS Appl Mater Interfaces, 2022, 14(33): 37396-37409.
4.	Gore JC. Artificial intelligence in medical imaging. Magn Reson Imaging, 2020, 68: A1-A4.
5.	Falini S, Angelotti G, Cecconi M. ICU management based on big data. Curr Opin Anaesthesiol, 2020, 33(2): 162-169.
6.	Pollard TJ, Johnson AEW, Raffa JD, et al. The eICU collaborative research database, a freely available multi-center database for critical care research. Sci Data, 2018, 5: 180178.
7.	Wu WT, Li YJ, Feng AZ, et al. Data mining in clinical big data: the frequently used databases, steps, and methodological models. Mil Med Res, 2021, 8(1): 44.
8.	Hong N, Liu C, Gao J, et al. State of the art of machine learning-enabled clinical decision support in intensive care units: literature review. JMIR Med Inform, 2022, 10(3): e28781.
9.	Arora G, Joshi J, Mandal RS, et al. Artificial intelligence in surveillance, diagnosis, drug discovery and vaccine development against COVID-19. Pathogens, 2021, 10(8): 1048.
10.	任全娥. 我国文献计量学研究40年—基于知识图谱的回顾与展望. 信息与管理研究, 2020, 5(Z2): 16-31.
11.	陈悦, 陈超美, 刘则渊, 等. CiteSpace知识图谱的方法论功能. 科学学研究, 2015, 33(2): 242-253.
12.	Johnson AE, Pollard TJ, Shen L, et al. MIMIC-III, a freely accessible critical care database. Sci Data, 2016, 3: 160035.
13.	Schwalbe N, Wahl B. Artificial intelligence and the future of global health. Lancet, 2020, 395(10236): 1579-1586.
14.	Beil M, Proft I, van Heerden D, et al. Ethical considerations about artificial intelligence for prognostication in intensive care. Intensive Care Med Exp, 2019, 7(1): 70.
15.	Wang J, Deng H, Liu B, et al. Systematic evaluation of research progress on natural language processing in medicine over the past 20 years: bibliometric study on PubMed. J Med Internet Res, 2020, 22(1): e16816.
16.	耿溪谣, 胡洋. 全球价值链视角下中国与印度信息产业国际竞争力的比较分析. 世界地理研究, 2022, 31(2): 270-279.
17.	薛博. 关于电子信息技术的发展现状及趋势. 信息系统工程, 2023, (4): 137-139.
18.	Seah JCY, Tang JSN, Kitchen A, et al. Chest radiographs in congestive heart failure: visualizing neural network learning. Radiology, 2019, 290(2): 514-522.
19.	Horng S, Liao R, Wang X, et al. Deep learning to quantify pulmonary edema in chest radiographs. Radiol Artif Intell, 2021, 3(2): e190228.
20.	Monteiro M, Newcombe VFJ, Mathieu F, et al. Multiclass semantic segmentation and quantification of traumatic brain injury lesions on head CT using deep learning: an algorithm development and multicentre validation study. Lancet Digit Health, 2020, 2(6): e314-e322.
21.	Chen L, Ogundele O, Clermont G, et al. Dynamic and personalized risk forecast in step-down units: implications for monitoring paradigms. Ann Am Thorac Soc, 2017, 14(3): 384-391.
22.	Joosten A, Rinehart J, Van der Linden P, et al. Computer-assisted individualized hemodynamic management reduces intraoperative hypotension in intermediate- and high-risk surgery: a randomized controlled trial. Anesthesiology, 2021, 135(2): 258-272.
23.	Lassau N, Ammari S, Chouzenoux E, et al. Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients. Nat Commun, 2021, 12(1): 634.
24.	Seymour CW, Kennedy JN, Wang S, et al. Derivation, validation, and potential treatment implications of novel clinical phenotypes for Sepsis. JAMA, 2019, 321(20): 2003-2017.
25.	Calfee CS, Delucchi KL, Sinha P, et al. Acute respiratory distress syndrome subphenotypes and differential response to simvastatin: secondary analysis of a randomised controlled trial. Lancet Respir Med, 2018, 6(9): 691-698.
26.	Gurovich Y. The path to and impact of disease recognition with AI. IEEE Pulse, 2020, 11(1): 13-16.
27.	Khanagar SB, Al-Ehaideb A, Vishwanathaiah S, et al. Scope and performance of artificial intelligence technology in orthodontic diagnosis, treatment planning, and clinical decision-making - a systematic review. J Dent Sci, 2021, 16(1): 482-492.
28.	袁波, 代华, 伍佳, 等. 人工智能在全科医学领域的应用. 中华全科医学, 2021, 19(9): 5.
29.	杨文静, 杜然然, 吕章艳, 等. 人工智能在疾病预测研究中可视化分析. 中国公共卫生, 2021, 37(5): 871-874.
30.	顾坚磊, 江建平, 田园, 等. 人工智能技术的应用: 罕见病临床决策系统的需求、现状与挑战. 第二军医大学学报, 2018, 39(8): 819-825.
31.	Zhang Z, Van Poucke S, Goyal H, et al. The top 2, 000 cited articles in critical care medicine: a bibliometric analysis. J Thorac Dis, 2018, 10(4): 2437-2447.
32.	van de Sande D, van Genderen ME, Huiskens J, et al. Moving from bytes to bedside: a systematic review on the use of artificial intelligence in the intensive care unit. Intensive Care Med, 2021, 47(7): 750-760.
33.	李梦薇, 高芳, 徐峰. 人工智能应用场景的成熟度评价研究. 情报杂志, 2022, 41(12): 176-183.
34.	Tang R, Zhang S, Ding C, et al. Artificial intelligence in intensive care medicine: bibliometric analysis. J Med Internet Res, 2022, 24(11): e42185.

1. Kulkarni S, Seneviratne N, Baig MS, et al. Artificial intelligence in medicine: where are we now. Acad Radiol, 2020, 27(1): 62-70.
2. Hassoun S, Jefferson F, Shi X, et al. Artificial intelligence for biology. Integr Comp Biol, 2022, 61(6): 2267-2275.
3. Xue Y, Chen C, Tan R, et al. Artificial intelligence-assisted bioinformatics, microneedle, and diabetic wound healing: a "new deal" of an old drug. ACS Appl Mater Interfaces, 2022, 14(33): 37396-37409.
4. Gore JC. Artificial intelligence in medical imaging. Magn Reson Imaging, 2020, 68: A1-A4.
5. Falini S, Angelotti G, Cecconi M. ICU management based on big data. Curr Opin Anaesthesiol, 2020, 33(2): 162-169.
6. Pollard TJ, Johnson AEW, Raffa JD, et al. The eICU collaborative research database, a freely available multi-center database for critical care research. Sci Data, 2018, 5: 180178.
7. Wu WT, Li YJ, Feng AZ, et al. Data mining in clinical big data: the frequently used databases, steps, and methodological models. Mil Med Res, 2021, 8(1): 44.
8. Hong N, Liu C, Gao J, et al. State of the art of machine learning-enabled clinical decision support in intensive care units: literature review. JMIR Med Inform, 2022, 10(3): e28781.
9. Arora G, Joshi J, Mandal RS, et al. Artificial intelligence in surveillance, diagnosis, drug discovery and vaccine development against COVID-19. Pathogens, 2021, 10(8): 1048.
10. 任全娥. 我国文献计量学研究40年—基于知识图谱的回顾与展望. 信息与管理研究, 2020, 5(Z2): 16-31.
11. 陈悦, 陈超美, 刘则渊, 等. CiteSpace知识图谱的方法论功能. 科学学研究, 2015, 33(2): 242-253.
12. Johnson AE, Pollard TJ, Shen L, et al. MIMIC-III, a freely accessible critical care database. Sci Data, 2016, 3: 160035.
13. Schwalbe N, Wahl B. Artificial intelligence and the future of global health. Lancet, 2020, 395(10236): 1579-1586.
14. Beil M, Proft I, van Heerden D, et al. Ethical considerations about artificial intelligence for prognostication in intensive care. Intensive Care Med Exp, 2019, 7(1): 70.
15. Wang J, Deng H, Liu B, et al. Systematic evaluation of research progress on natural language processing in medicine over the past 20 years: bibliometric study on PubMed. J Med Internet Res, 2020, 22(1): e16816.
16. 耿溪谣, 胡洋. 全球价值链视角下中国与印度信息产业国际竞争力的比较分析. 世界地理研究, 2022, 31(2): 270-279.
17. 薛博. 关于电子信息技术的发展现状及趋势. 信息系统工程, 2023, (4): 137-139.
18. Seah JCY, Tang JSN, Kitchen A, et al. Chest radiographs in congestive heart failure: visualizing neural network learning. Radiology, 2019, 290(2): 514-522.
19. Horng S, Liao R, Wang X, et al. Deep learning to quantify pulmonary edema in chest radiographs. Radiol Artif Intell, 2021, 3(2): e190228.
20. Monteiro M, Newcombe VFJ, Mathieu F, et al. Multiclass semantic segmentation and quantification of traumatic brain injury lesions on head CT using deep learning: an algorithm development and multicentre validation study. Lancet Digit Health, 2020, 2(6): e314-e322.
21. Chen L, Ogundele O, Clermont G, et al. Dynamic and personalized risk forecast in step-down units: implications for monitoring paradigms. Ann Am Thorac Soc, 2017, 14(3): 384-391.
22. Joosten A, Rinehart J, Van der Linden P, et al. Computer-assisted individualized hemodynamic management reduces intraoperative hypotension in intermediate- and high-risk surgery: a randomized controlled trial. Anesthesiology, 2021, 135(2): 258-272.
23. Lassau N, Ammari S, Chouzenoux E, et al. Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients. Nat Commun, 2021, 12(1): 634.
24. Seymour CW, Kennedy JN, Wang S, et al. Derivation, validation, and potential treatment implications of novel clinical phenotypes for Sepsis. JAMA, 2019, 321(20): 2003-2017.
25. Calfee CS, Delucchi KL, Sinha P, et al. Acute respiratory distress syndrome subphenotypes and differential response to simvastatin: secondary analysis of a randomised controlled trial. Lancet Respir Med, 2018, 6(9): 691-698.
26. Gurovich Y. The path to and impact of disease recognition with AI. IEEE Pulse, 2020, 11(1): 13-16.
27. Khanagar SB, Al-Ehaideb A, Vishwanathaiah S, et al. Scope and performance of artificial intelligence technology in orthodontic diagnosis, treatment planning, and clinical decision-making - a systematic review. J Dent Sci, 2021, 16(1): 482-492.
28. 袁波, 代华, 伍佳, 等. 人工智能在全科医学领域的应用. 中华全科医学, 2021, 19(9): 5.
29. 杨文静, 杜然然, 吕章艳, 等. 人工智能在疾病预测研究中可视化分析. 中国公共卫生, 2021, 37(5): 871-874.
30. 顾坚磊, 江建平, 田园, 等. 人工智能技术的应用: 罕见病临床决策系统的需求、现状与挑战. 第二军医大学学报, 2018, 39(8): 819-825.
31. Zhang Z, Van Poucke S, Goyal H, et al. The top 2, 000 cited articles in critical care medicine: a bibliometric analysis. J Thorac Dis, 2018, 10(4): 2437-2447.
32. van de Sande D, van Genderen ME, Huiskens J, et al. Moving from bytes to bedside: a systematic review on the use of artificial intelligence in the intensive care unit. Intensive Care Med, 2021, 47(7): 750-760.
33. 李梦薇, 高芳, 徐峰. 人工智能应用场景的成熟度评价研究. 情报杂志, 2022, 41(12): 176-183.
34. Tang R, Zhang S, Ding C, et al. Artificial intelligence in intensive care medicine: bibliometric analysis. J Med Internet Res, 2022, 24(11): e42185.

Chinese Journal of Evidence-Based Medicine

The application of artificial intelligence technology in intensive care medicine in the last ten years: a visualization analysis

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