Bibliometric analysis of the application of artificial intelligence in the field of healthcare-associated infection_West China Medical Journal

Authors：

LIU Min , CHEN Fang , DENG Rong ,  ZHAO Jing

1. Center of Infectious Diseases, West China Hospital, Sichuan University / West China School of Nursing, Sichuan University, Chengdu, Sichuan 610041, P. R. China;

Corresponding author：

ZHAO Jing, Email: 649392655@qq.com

Keywords：

Healthcare-associated infection; artificial intelligence; bibliometric analysis; CiteSpace

DOI：

10.7507/1002-0179.202402084

Video：

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Abstract Full text Figures/Tables Video References Cited by

Objective To use bibliometrics to identify research hotspots and emerging trends in the use of artificial intelligence (AI) in healthcare-associated infections (HAI), as well as to offer a resource for more relevant research. Methods The literature on AI and HAI from the Science Citation Index Expanded database of the Web of Science Core Collection was retrieved through computer searches, covering the period from January 1, 1994, to January 22, 2024. VOSviewer (v1.6.19) and CiteSpace (v6.1. R6) software were utilized for bibliometric analysis, creating knowledge maps that include research cooperation networks and keyword analysis. Results A total of 305 documents were included, and both the number of early publications and the frequency of citations were at a very low level for a long time before showing an annual increase trend after 2018. The United States had the most published documents among the 50 countries/regions from where they were sourced. Harvard University was the scientific research institution with the most publications, while Professor Evans HL of the Medical University of South Carolina was the scholar with the most publications. Research on AI in the field of HAI primarily focused on three aspects: AI algorithms and technologies, monitoring and prediction of HAI, and the accuracy of HAI diagnosis and prediction. These findings were based on keyword co-occurrence and clustering analysis. Conclusions A new phase of AI research in the subject of HAI has begun. More in-depth research can be done in the future for the hot direction, as there is still a gap between China’s academic accomplishments in this subject and the advanced level of the world.

Citation： LIU Min, CHEN Fang, DENG Rong, ZHAO Jing. Bibliometric analysis of the application of artificial intelligence in the field of healthcare-associated infection. West China Medical Journal, 2024, 39(3): 399-405. doi: 10.7507/1002-0179.202402084 Copy

1.	Jenkins DR. Nosocomial infections and infection control. Medicine, 2017, 45(10): 629-633.
2.	Raoofi S, Pashazadeh Kan F, Rafiei S, et al. Global prevalence of nosocomial infection: a systematic review and meta-analysis. PLoS One, 2023, 18(1): e0274248.
3.	Alemu AY, Endalamaw A, Bayih WA. The burden of healthcare-associated infection in Ethiopia: a systematic review and meta-analysis. Trop Med Health, 2020, 48: 77.
4.	Saleem Z, Godman B, Hassali MA, et al. Point prevalence surveys of health-care-associated infections: a systematic review. Pathog Glob Health, 2019, 113(4): 191-205.
5.	丁梦媛, 李文进, 耿苗苗, 等. 耐药菌医院感染风险评估与管理研究进展. 中国卫生资源, 2020, 23(4): 378-383.
6.	Theodosiou AA, Read RC. Artificial intelligence, machine learning and deep learning: potential resources for the infection clinician. J Infect, 2023, 87(4): 287-294.
7.	Donthu N, Kumar S, Mukherjee D, et al. How to conduct a bibliometric analysis: an overview and guidelines. J Bus Res, 2021, 133: 285-296.
8.	He Z, Dai L, Zuo Y, et al. Hotspots and frontiers in pulmonary arterial hypertension research: a bibliometric and visualization analysis from 2011 to 2020. Bioengineered, 2022, 13(6): 14667-14680.
9.	Lv Z, Hou J, Wang Y, et al. Knowledge-map analysis of bladder cancer immunotherapy. Hum Vaccin Immunother, 2023, 19(3): 2267301.
10.	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.
11.	陈元, 董四平, 刘庭芳. 基于 CiteSpace 的医院运营管理英文文献计量分析. 华西医学, 2023, 38(12): 1850-1856.
12.	Wu M, Long R, Bai Y, et al. Knowledge mapping analysis of international research on environmental communication using bibliometrics. J Environ Manage, 2021, 298: 113475.
13.	Weissglass DE. Contextual bias, the democratization of healthcare, and medical artificial intelligence in low- and middle-income countries. Bioethics, 2022, 36(2): 201-209.
14.	Guo Y, Hao Z, Zhao S, et al. Artificial intelligence in health care: bibliometric analysis. J Med Internet Res, 2020, 22(7): e18228.
15.	Wondimagegn D, Ragab L, Yifter H, et al. Breaking borders: how barriers to global mobility hinder international partnerships in academic medicine. Acad Med, 2022, 97(1): 37-40.
16.	Kotiranta A, Tahvanainen A, Kovalainen A, et al. Forms and varieties of research and industry collaboration across disciplines. Heliyon, 2020, 6(3): e03404.
17.	Hautemanière A, Florentin A, Hartemann P, et al. Identifying possible deaths associated with nosocomial infection in a hospital by data mining. Am J Infect Control, 2011, 39(2): 118-122.
18.	Sohn S, Larson DW, Habermann EB, et al. Detection of clinically important colorectal surgical site infection using Bayesian network. J Surg Res, 2017, 209: 168-173.
19.	Liao YH, Wang ZC, Zhang FG, et al. Machine Learning methods applied to predict ventilator-associated pneumonia with Pseudomonas aeruginosa infection via sensor array of electronic nose in intensive care unit. Sensors (Basel), 2019, 19(8): 1866.
20.	Karhade AV, Bongers MER, Groot OQ, et al. Can natural language processing provide accurate, automated reporting of wound infection requiring reoperation after lumbar discectomy?. Spine J, 2020, 20(10): 1602-1609.
21.	Shrimali S, Teuscher C. A novel deep learning-, camera-, and sensor-based system for enforcing hand hygiene compliance in healthcare facilities. IEEE Sens J, 2023, 23(12): 13659-13670.
22.	Carneiro J, Magalhães RP, de la Oliva Roque VM, et al. TargIDe: a machine-learning workflow for target identification of molecules with antibiofilm activity against Pseudomonas aeruginosa. J Comput Aid Mol Des, 2023, 37(5/6): 265-278.
23.	Dang T, Han J, Xia T, et al. Exploring longitudinal cough, breath, and voice data for COVID-19 progression prediction via sequential deep learning: model development and validation. J Med Internet Res, 2022, 24(6): e37004.
24.	Watine J, Charet JC, Bruel A, et al. Usefulness of a computerized expert system associated with systematic O-serotyping for the early detection of outbreaks of hospital acquired infections and for the presumptive antibiotic therapy of Pseudomonas aeruginosa infections. Pathol Biol (Paris), 1996, 44(2): 125-131.
25.	Sureyya Rifaioglu A, Doğan T, Jesus Martin M, et al. DEEPred: automated protein function prediction with multi-task feed-forward deep neural networks. Sci Rep, 2019, 9(1): 7344.
26.	Lewin-Epstein O, Baruch S, Hadany L, et al. Predicting antibiotic resistance in hospitalized patients by applying machine learning to electronic medical records. Clin Infect Dis, 2021, 72(11): e848-e855.
27.	Mendonça EA, Haas J, Shagina L, et al. Extracting information on pneumonia in infants using natural language processing of radiology reports. J Biomed Inform, 2005, 38(4): 314-321.
28.	Mikulskis P, Hook A, Dundas AA, et al. Prediction of broad-spectrum pathogen attachment to coating materials for biomedical devices. Acs Appl Mater Inter, 2018, 10(1): 139-149.
29.	Li Y, Wang Y. Temporal convolution attention model for sepsis clinical assistant diagnosis prediction. Math Biosci Eng, 2023, 20(7): 13356-13378.
30.	Roimi M, Neuberger A, Shrot A, et al. Early diagnosis of bloodstream infections in the intensive care unit using machine-learning algorithms. Intens Care Med, 2020, 46(3): 454-462.
31.	Tabaie A, Orenstein EW, Kandaswamy S, et al. Integrating structured and unstructured data for timely prediction of bloodstream infection among children. Pediatr Res, 2023, 93(4): 969-975.
32.	Tabaie A, Orenstein EW, Nemati S, et al. Deep learning model to predict serious infection among children with central venous lines. Front Pediatr, 2021, 9: 726870.
33.	Sanger PC, van Ramshorst GH, Mercan E, et al. A prognostic model of surgical site infection using daily clinical wound assessment. J Am Coll Surgeons, 2016, 223(2): 259-270. e2.
34.	Chen WJ, Lu Z, You L, et al. Artificial intelligence-based multimodal risk assessment model for surgical site infection (AMRAMS): development and validation study. JMIR Med Inf, 2020, 8(6): e18186.
35.	Lo ZJ, Mak MHW, Liang S, et al. Development of an explainable artificial intelligence model for Asian vascular wound images. Int Wound J, 2023: 14.
36.	Wiens J, Guttag J, Horvitz E. Patient risk stratification with time-varying parameters: a multitask learning approach. J Mach Learn Res, 2016, 17: 23.
37.	Noaman AY, Nadeem F, Ragab AHM, et al. Improving prediction accuracy of “central line-associated blood stream infections” using data mining models. Biomed Res Int, 2017: 3292849.
38.	Xu J, Chen X, Zheng X. Acinetobacter baumannii complex-caused bloodstream infection in ICU during a 12-year period: predicting fulminant sepsis by interpretable machine learning. Front Microbiol, 2022, 13: 1037735.

1. Jenkins DR. Nosocomial infections and infection control. Medicine, 2017, 45(10): 629-633.
2. Raoofi S, Pashazadeh Kan F, Rafiei S, et al. Global prevalence of nosocomial infection: a systematic review and meta-analysis. PLoS One, 2023, 18(1): e0274248.
3. Alemu AY, Endalamaw A, Bayih WA. The burden of healthcare-associated infection in Ethiopia: a systematic review and meta-analysis. Trop Med Health, 2020, 48: 77.
4. Saleem Z, Godman B, Hassali MA, et al. Point prevalence surveys of health-care-associated infections: a systematic review. Pathog Glob Health, 2019, 113(4): 191-205.
5. 丁梦媛, 李文进, 耿苗苗, 等. 耐药菌医院感染风险评估与管理研究进展. 中国卫生资源, 2020, 23(4): 378-383.
6. Theodosiou AA, Read RC. Artificial intelligence, machine learning and deep learning: potential resources for the infection clinician. J Infect, 2023, 87(4): 287-294.
7. Donthu N, Kumar S, Mukherjee D, et al. How to conduct a bibliometric analysis: an overview and guidelines. J Bus Res, 2021, 133: 285-296.
8. He Z, Dai L, Zuo Y, et al. Hotspots and frontiers in pulmonary arterial hypertension research: a bibliometric and visualization analysis from 2011 to 2020. Bioengineered, 2022, 13(6): 14667-14680.
9. Lv Z, Hou J, Wang Y, et al. Knowledge-map analysis of bladder cancer immunotherapy. Hum Vaccin Immunother, 2023, 19(3): 2267301.
10. 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.
11. 陈元, 董四平, 刘庭芳. 基于 CiteSpace 的医院运营管理英文文献计量分析. 华西医学, 2023, 38(12): 1850-1856.
12. Wu M, Long R, Bai Y, et al. Knowledge mapping analysis of international research on environmental communication using bibliometrics. J Environ Manage, 2021, 298: 113475.
13. Weissglass DE. Contextual bias, the democratization of healthcare, and medical artificial intelligence in low- and middle-income countries. Bioethics, 2022, 36(2): 201-209.
14. Guo Y, Hao Z, Zhao S, et al. Artificial intelligence in health care: bibliometric analysis. J Med Internet Res, 2020, 22(7): e18228.
15. Wondimagegn D, Ragab L, Yifter H, et al. Breaking borders: how barriers to global mobility hinder international partnerships in academic medicine. Acad Med, 2022, 97(1): 37-40.
16. Kotiranta A, Tahvanainen A, Kovalainen A, et al. Forms and varieties of research and industry collaboration across disciplines. Heliyon, 2020, 6(3): e03404.
17. Hautemanière A, Florentin A, Hartemann P, et al. Identifying possible deaths associated with nosocomial infection in a hospital by data mining. Am J Infect Control, 2011, 39(2): 118-122.
18. Sohn S, Larson DW, Habermann EB, et al. Detection of clinically important colorectal surgical site infection using Bayesian network. J Surg Res, 2017, 209: 168-173.
19. Liao YH, Wang ZC, Zhang FG, et al. Machine Learning methods applied to predict ventilator-associated pneumonia with Pseudomonas aeruginosa infection via sensor array of electronic nose in intensive care unit. Sensors (Basel), 2019, 19(8): 1866.
20. Karhade AV, Bongers MER, Groot OQ, et al. Can natural language processing provide accurate, automated reporting of wound infection requiring reoperation after lumbar discectomy?. Spine J, 2020, 20(10): 1602-1609.
21. Shrimali S, Teuscher C. A novel deep learning-, camera-, and sensor-based system for enforcing hand hygiene compliance in healthcare facilities. IEEE Sens J, 2023, 23(12): 13659-13670.
22. Carneiro J, Magalhães RP, de la Oliva Roque VM, et al. TargIDe: a machine-learning workflow for target identification of molecules with antibiofilm activity against Pseudomonas aeruginosa. J Comput Aid Mol Des, 2023, 37(5/6): 265-278.
23. Dang T, Han J, Xia T, et al. Exploring longitudinal cough, breath, and voice data for COVID-19 progression prediction via sequential deep learning: model development and validation. J Med Internet Res, 2022, 24(6): e37004.
24. Watine J, Charet JC, Bruel A, et al. Usefulness of a computerized expert system associated with systematic O-serotyping for the early detection of outbreaks of hospital acquired infections and for the presumptive antibiotic therapy of Pseudomonas aeruginosa infections. Pathol Biol (Paris), 1996, 44(2): 125-131.
25. Sureyya Rifaioglu A, Doğan T, Jesus Martin M, et al. DEEPred: automated protein function prediction with multi-task feed-forward deep neural networks. Sci Rep, 2019, 9(1): 7344.
26. Lewin-Epstein O, Baruch S, Hadany L, et al. Predicting antibiotic resistance in hospitalized patients by applying machine learning to electronic medical records. Clin Infect Dis, 2021, 72(11): e848-e855.
27. Mendonça EA, Haas J, Shagina L, et al. Extracting information on pneumonia in infants using natural language processing of radiology reports. J Biomed Inform, 2005, 38(4): 314-321.
28. Mikulskis P, Hook A, Dundas AA, et al. Prediction of broad-spectrum pathogen attachment to coating materials for biomedical devices. Acs Appl Mater Inter, 2018, 10(1): 139-149.
29. Li Y, Wang Y. Temporal convolution attention model for sepsis clinical assistant diagnosis prediction. Math Biosci Eng, 2023, 20(7): 13356-13378.
30. Roimi M, Neuberger A, Shrot A, et al. Early diagnosis of bloodstream infections in the intensive care unit using machine-learning algorithms. Intens Care Med, 2020, 46(3): 454-462.
31. Tabaie A, Orenstein EW, Kandaswamy S, et al. Integrating structured and unstructured data for timely prediction of bloodstream infection among children. Pediatr Res, 2023, 93(4): 969-975.
32. Tabaie A, Orenstein EW, Nemati S, et al. Deep learning model to predict serious infection among children with central venous lines. Front Pediatr, 2021, 9: 726870.
33. Sanger PC, van Ramshorst GH, Mercan E, et al. A prognostic model of surgical site infection using daily clinical wound assessment. J Am Coll Surgeons, 2016, 223(2): 259-270. e2.
34. Chen WJ, Lu Z, You L, et al. Artificial intelligence-based multimodal risk assessment model for surgical site infection (AMRAMS): development and validation study. JMIR Med Inf, 2020, 8(6): e18186.
35. Lo ZJ, Mak MHW, Liang S, et al. Development of an explainable artificial intelligence model for Asian vascular wound images. Int Wound J, 2023: 14.
36. Wiens J, Guttag J, Horvitz E. Patient risk stratification with time-varying parameters: a multitask learning approach. J Mach Learn Res, 2016, 17: 23.
37. Noaman AY, Nadeem F, Ragab AHM, et al. Improving prediction accuracy of “central line-associated blood stream infections” using data mining models. Biomed Res Int, 2017: 3292849.
38. Xu J, Chen X, Zheng X. Acinetobacter baumannii complex-caused bloodstream infection in ICU during a 12-year period: predicting fulminant sepsis by interpretable machine learning. Front Microbiol, 2022, 13: 1037735.

West China Medical Journal

Bibliometric analysis of the application of artificial intelligence in the field of healthcare-associated infection

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