244
Views
1
CrossRef citations to date
0
Altmetric
ORIGINAL RESEARCH

Research Hotspots and Trends of Deep Learning in Critical Care Medicine: A Bibliometric and Visualized Study

ORCID Icon, ORCID Icon, , & ORCID Icon
Pages 2155-2166 | Received 09 May 2023, Accepted 25 Jul 2023, Published online: 29 Jul 2023

References

  • Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Commun ACM. 2017;60:84–90. doi:10.1145/3065386
  • Girshick R, Donahue J, Darrell T, Malik J. Rich feature hierarchies for accurate object detection and semantic segmentation. 27th IEEE Conference on Computer Vision and Pattern Recognition (CVPR); Columbus, OH; 2014.
  • Goecks J, Jalili V, Heiser LM, Gray JW. How machine learning will transform biomedicine. Cell. 2020;181(1):92–101. doi:10.1016/j.cell.2020.03.022
  • Shinde P, Shah S. A review of machine learning and deep learning applications; 2019.
  • Irisson JO, Ayata SD, Lindsay DJ, Karp-Boss L, Stemmann L. Machine learning for the study of plankton and marine snow from images. Ann Rev Mar Sci. 2022;14:277–301. doi:10.1146/annurev-marine-041921-013023
  • Wang S, Yang DM, Rong R, Zhan X, Xiao G. Pathology image analysis using segmentation deep learning algorithms. Am J Pathol. 2019;189(9):1686–1698. doi:10.1016/j.ajpath.2019.05.007
  • Egger J, Gsaxner C, Pepe A, et al. Medical deep learning-A systematic meta-review. Comput Methods Programs Biomed. 2022;221:106874. doi:10.1016/j.cmpb.2022.106874
  • Zhang Z, Pan Q, Ge H, Xing L, Hong Y, Chen P. Deep learning-based clustering robustly identified two classes of sepsis with both prognostic and predictive values. EBioMedicine. 2020;62:103081. doi:10.1016/j.ebiom.2020.103081
  • Reamaroon N, Sjoding MW, Gryak J, Athey BD, Najarian K, Derksen H. Automated detection of acute respiratory distress syndrome from chest X-rays using directionality measure and deep learning features. Comput Biol Med. 2021;134:104463. doi:10.1016/j.compbiomed.2021.104463
  • Sharma N, Simmons LH, Jones PS, et al. Motor imagery after subcortical stroke: a functional magnetic resonance imaging study. Stroke. 2009;40(4):1315–1324. doi:10.1161/STROKEAHA.108.525766
  • Lauritsen SM, Kalør ME, Kongsgaard EL, et al. Early detection of sepsis utilizing deep learning on electronic health record event sequences. Artif Intell Med. 2020;104:101820. doi:10.1016/j.artmed.2020.101820
  • Aşuroğlu T, Oğul H. A deep learning approach for sepsis monitoring via severity score estimation. Comput Methods Programs Biomed. 2021;198:105816. doi:10.1016/j.cmpb.2020.105816
  • Jia Y, Kaul C, Lawton T, Murray-Smith R, Habli I. Prediction of weaning from mechanical ventilation using convolutional neural networks. Artif Intell Med. 2021;117:102087. doi:10.1016/j.artmed.2021.102087
  • Yeung S, Rinaldo F, Jopling J, et al. A computer vision system for deep learning-based detection of patient mobilization activities in the ICU. NPJ Digit Med. 2019;2:11. doi:10.1038/s41746-019-0087-z
  • Tang H, Jin Z, Deng J, et al. Development and validation of a deep learning model to predict the survival of patients in ICU. J Am Med Inform Assoc. 2022;29(9):1567–1576. doi:10.1093/jamia/ocac098
  • Datta R, Singh S. Artificial intelligence in critical care: its about time! Med J Armed Forces India. 2021;77(3):266–275. doi:10.1016/j.mjafi.2020.10.005
  • Danış F, Kudu E. The evolution of cardiopulmonary resuscitation: global productivity and publication trends. Am J Emerg Med. 2022;54:151–164. doi:10.1016/j.ajem.2022.01.071
  • Niu B, Hong S, Yuan J, Peng S, Wang Z, Zhang X. Global trends in sediment-related research in earth science during 1992–2011: a bibliometric analysis. Scientometrics. 2013;98(1):511–529. doi:10.1007/s11192-013-1065-x
  • LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436–444. doi:10.1038/nature14539
  • Guo Y, Hao Z, Zhao S, Gong J, Yang F. Artificial intelligence in health care: bibliometric analysis. J Med Internet Res. 2020;22(7):e18228. doi:10.2196/18228
  • Ozrazgat-Baslanti T, Loftus TJ, Ren Y, Ruppert MM, Bihorac A. Advances in artificial intelligence and deep learning systems in ICU-related acute kidney injury. Curr Opin Crit Care. 2021;27(6):560–572. doi:10.1097/MCC.0000000000000887
  • Ching T, Himmelstein DS, Beaulieu-Jones BK, et al. Opportunities and obstacles for deep learning in biology and medicine. J R Soc Interface. 2018;15(141):20170387.
  • Miotto R, Wang F, Wang S, Jiang X, Dudley JT. Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform. 2018;19(6):1236–1246. doi:10.1093/bib/bbx044
  • Wang L, Tong L, Davis D, Arnold T, Esposito T. The application of unsupervised deep learning in predictive models using electronic health records. BMC Med Res Methodol. 2020;20(1):37. doi:10.1186/s12874-020-00923-1
  • Golas SB, Shibahara T, Agboola S, et al. A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data. BMC Med Inform Decis Mak. 2018;18(1):44. doi:10.1186/s12911-018-0620-z
  • Harvey HB, Gowda V. Regulatory issues and challenges to artificial intelligence adoption. Radiol Clin North Am. 2021;59(6):1075–1083. doi:10.1016/j.rcl.2021.07.007
  • Zhong L, Dong D, Fang X, et al. A deep learning-based radiomic nomogram for prognosis and treatment decision in advanced nasopharyngeal carcinoma: a multicentre study. EBioMedicine. 2021;70:103522. doi:10.1016/j.ebiom.2021.103522
  • Hiremath A, Shiradkar R, Fu P, et al. An integrated nomogram combining deep learning, Prostate Imaging-Reporting and Data System (PI-RADS) scoring, and clinical variables for identification of clinically significant prostate cancer on biparametric MRI: a retrospective multicentre study. Lancet Digit Health. 2021;3(7):e445–e454. doi:10.1016/S2589-7500(21)00082-0
  • Pollard TJ, Johnson AE, Raffa JD, Celi LA, Mark RG, Badawi O. The eICU collaborative research database, a freely available multi-center database for critical care research. Sci Data. 2018;5:180178. doi:10.1038/sdata.2018.178
  • Johnson AE, Pollard TJ, Shen L, et al. MIMIC-III, a freely accessible critical care database. Sci Data. 2016;3:160035. doi:10.1038/sdata.2016.35
  • Guo F, Zhu X, Wu Z, Zhu L, Wu J, Zhang F. Clinical applications of machine learning in the survival prediction and classification of sepsis: coagulation and heparin usage matter. J Transl Med. 2022;20(1):265. doi:10.1186/s12967-022-03469-6
  • Alfieri F, Ancona A, Tripepi G, et al. External validation of a deep-learning model to predict severe acute kidney injury based on urine output changes in critically ill patients. J Nephrol. 2022;35(8):2047–2056. doi:10.1007/s40620-022-01335-8
  • Wu J, Lin Y, Li P, Hu Y, Zhang L, Kong G. Predicting Prolonged Length of ICU stay through machine learning. Diagnostics. 2021;11(12):2242. doi:10.3390/diagnostics11122242
  • Pishgar M, Theis J, Del Rios M, Ardati A, Anahideh H, Darabi H. Prediction of unplanned 30-day readmission for ICU patients with heart failure. BMC Med Inform Decis Mak. 2022;22(1):117. doi:10.1186/s12911-022-01857-y
  • McMaster C, Chan J, Liew DFL, et al. Developing a deep learning natural language processing algorithm for automated reporting of adverse drug reactions. J Biomed Inform. 2023;137:104265. doi:10.1016/j.jbi.2022.104265
  • Röösli E, Bozkurt S, Hernandez-Boussard T. Peeking into a black box, the fairness and generalizability of a MIMIC-III benchmarking model. Sci Data. 2022;9(1):24. doi:10.1038/s41597-021-01110-7
  • Qiang W, Xiao C, Li Z, et al. Impactful publications of critical care medicine research in China: a bibliometric analysis. Front Med. 2022;9:974025. doi:10.3389/fmed.2022.974025
  • Liu YX, Zhu C, Wu ZX, Lu LJ, Yu YT. A bibliometric analysis of the application of artificial intelligence to advance individualized diagnosis and treatment of critical illness. Ann Transl Med. 2022;10(16):854. doi:10.21037/atm-22-913
  • Cui X, Chang Y, Yang C, Cong Z, Wang B, Leng Y. Development and trends in artificial intelligence in critical care medicine: a bibliometric analysis of related research over the period of 2010–2021. J Pers Med. 2022;13(1):50. doi:10.3390/jpm13010050
  • Castelluccia Claude, le Me´tayer Daniel, European Parliament. European Parliamentary Research Service. Scientific foresight unit. Understanding algorithmic decision-making: opportunities and challenges; 2019. Available from: https://www.europarl.europa.eu/RegData/etudes/STUD/2019/624261/EPRS_STU. Accessed January 27, 2023.
  • Yoon JH, Pinsky MR, Clermont G. Artificial intelligence in critical care medicine. Crit Care. 2022;26(1):75. doi:10.1186/s13054-022-03915-3
  • Caruso PF, Greco M, Ebm C, Angelotti G, Cecconi M. Implementing artificial intelligence: assessing the cost and benefits of algorithmic decision-making in critical care. Crit Care Clin. 2023;6072(1):1–253.
  • Ethics and governance of artificial intelligence for health ethics and governance of artificial intelligence for health 2; 2021. Available from: http://apps.who.int/bookorders. Accessed January 27, 2023.
  • Wu Q, Liu S, Zhang R, et al. ACU&MOX-DATA: a platform for fusion analysis and visual display acupuncture multi-omics heterogeneous data. Acupunct Herbal Med. 2023;3(1):59–62. doi:10.1097/HM9.0000000000000051
  • Jiang C, Qu H. In-line spectroscopy combined with multivariate analysis methods for endpoint determination in column chromatographic adsorption processes for herbal medicine. Acupunct Herbal Med. 2022;2(4):253–260. doi:10.1097/HM9.0000000000000035
  • Arabi YM, Myatra SN, Lobo SM. Surging ICU during COVID-19 pandemic: an overview. Curr Opin Crit Care. 2022;28(6):638–644. doi:10.1097/MCC.0000000000001001
  • Machová K, Mach M, Porezaný M. Deep learning in the detection of disinformation about COVID-19 in online space. Sensors. 2022;22(23):9319. doi:10.3390/s22239319