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Journal of Clinical Monitoring and Computing

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29.08.2019 | Commentary

Perioperative intelligence: applications of artificial intelligence in perioperative medicine

verfasst von: Kamal Maheshwari, Kurt Ruetzler, Bernd Saugel

Erschienen in: Journal of Clinical Monitoring and Computing | Ausgabe 4/2020

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Excerpt

Over the past decades, we have made tremendous strides in reducing intraoperative mortality but postoperative morbidity is still high and overall surgical care is costly [1, 2]. Novel technologies like machine learning [3], artificial intelligence [4], and big data [5] may help deliver appropriate and safe perioperative care. But there is lot of hype and it is not clear how. Perioperative intelligence provides a framework for collaborative work to deliver safe, timely and affordable perioperative care using artificial intelligence; it focuses on three key domains—identification of at-risk patients, early detection of complications, and timely and effective treatment. In other words perioperative intelligence is an application of artificial intelligence in perioperative medicine. …

Nepogodiev D, Martin J, Biccard B, Makupe A, Bhangu A. National Institute for Health Research Global Health Research Unit on Global S. Global burden of postoperative death. Lancet. 2019;393(10170):401.CrossRef

Meara JG, Leather AJ, Hagander L, Alkire BC, Alonso N, Ameh EA, et al. Global Surgery 2030: evidence and solutions for achieving health, welfare, and economic development. Lancet. 2015;386(9993):569–624.CrossRef

Obermeyer Z, Emanuel EJ. Predicting the future—big data, machine learning, and clinical medicine. N Engl J Med. 2016;375(13):1216–9.CrossRef

Gambus P, Shafer SL. Artificial intelligence for everyone. Anesthesiology. 2018;128(3):431–3.CrossRef

Sessler DI. Big data-and its contributions to peri-operative medicine. Anaesthesia. 2014;69(2):100–5.CrossRef

analytics S. Big data history and current considerations. 2019. https://www.sas.com/en_us/insights/big-data/what-is-big-data.html.

PhD JB. A Tour of Machine Learning Algorithms. 2013. https://s3.amazonaws.com/MLMastery/MachineLearningAlgorithms.png?__s=gqqpsae2gm6bbuqjg4ka.

Rajkomar A, Oren E, Chen K, Dai AM, Hajaj N, Hardt M, et al. Scalable and accurate deep learning with electronic health records. NPJ Digital Med. 2018;1(1):18.CrossRef

Lee CK, Hofer I, Gabel E, Baldi P, Cannesson M. Development and validation of a deep neural network model for prediction of postoperative in-hospital mortality. Anesthesiology. 2018;129(4):649–62.CrossRef

10.

Ratcliffe S. Oxford essential quotations. Oxford: Oxford University Press; 2016.CrossRef

11.

12.

Hatib F, Jian Z, Buddi S, Lee C, Settels J, Sibert K, et al. Machine-learning algorithm to predict hypotension based on high-fidelity arterial pressure waveform analysis. Anesthesiology. 2018;129(4):663–74.CrossRef

13.

Kendale S, Kulkarni P, Rosenberg AD, Wang J. Supervised machine-learning predictive analytics for prediction of postinduction hypotension. Anesthesiology. 2018;129(4):675–88.CrossRef

14.

Lee HC, Ryu HG, Chung EJ, Jung CW. Prediction of bispectral index during target-controlled infusion of propofol and remifentanil: a deep learning approach. Anesthesiology. 2018;128(3):492–501.CrossRef

15.

Maheshwari K, Cywinski J, Mathur P, Cummings KC, 3rd, Avitsian R, Crone T, et al. Identify and monitor clinical variation using machine intelligence: a pilot in colorectal surgery. J Clin Monit Comput. 2019;33(4):725–31.

16.

Keyang Xu ML, Jingzhi Pang, Xin Gao, Charlotte Band, Piyush Mathur MD, Frank Papay MD, Ashish K. Khanna MD, Jacek B. Cywinski MD, Kamal Maheshwari MD, Pengtao Xie, Eric Xing. Multimodal machine learning for automated ICD coding 2018 [cited 2018 7 Dec]. https://arxiv.org/abs/1810.13348.

Titel: Perioperative intelligence: applications of artificial intelligence in perioperative medicine
verfasst von: Kamal Maheshwari
Kurt Ruetzler
Bernd Saugel
Publikationsdatum: 29.08.2019
Verlag: Springer Netherlands
Erschienen in: Journal of Clinical Monitoring and Computing / Ausgabe 4/2020
Print ISSN: 1387-1307
Elektronische ISSN: 1573-2614
DOI: https://doi.org/10.1007/s10877-019-00379-9

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Perioperative intelligence: applications of artificial intelligence in perioperative medicine

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