Artificial intelligence and machine learning in intensive care unit

Ramaniuk T.I.

Philips LLC

Pozdnyakov D.Yu.

Philips LLC

Mushenok F.B.

Philips Innovation Laboratories Rus

Artificial intelligence and machine learning in intensive care unit

Authors:

Ramaniuk T.I., Pozdnyakov D.Yu., Mushenok F.B.

More about the authors

Journal: Russian Journal of Anesthesiology and Reanimatology. 2021;(4): 97‑104

DOI: 10.17116/anaesthesiology202104197

Downloaded: 218

Download PDF (RU)

Contact the author

Table of contents

To cite this article:

Ramaniuk TI, Pozdnyakov DYu, Mushenok FB. Artificial intelligence and machine learning in intensive care unit. Russian Journal of Anesthesiology and Reanimatology. 2021;(4):97‑104. (In Russ.)
https://doi.org/10.17116/anaesthesiology202104197

Close metadata

Recommended articles:

CAD EYE for real-time detection and differentiation of colorectal lesions. Russian Journal of Evidence-Based Gastroenterology. 2024;(2):50-54

Predicting the risk of decreased ovarian reserve after surgical treatment of patients with deep infiltrative endometriosis using artificial intelligence. Russian Bulletin of Obstetrician-Gynecologist. 2024;(3):92-102

Artificial intelligence in dermatology: opportunities and prospects. Russian Journal of Clinical Dermatology and Venereology. 2024;(3):246-252

Application of artificial intelligence in glaucoma. Part 1. Neural networks and deep learning in glaucoma screening and diagnosis. Russian Annals of Ophthalmology. 2024;(3):82-87

Efficacy of intensive therapy for massive intraoperative blood loss in children: experience of the Morozov Hospital. Pirogov Russian Journal of Surgery. 2024;(7):103-110

Autonomous artificial intelligence for sorting the preventive imaging studies’ results. Russian Journal of Preventive Medicine. 2024;(7):23-29

Diagnosis of benign laryngeal tumors using neural network. Russian Bulletin of Otorhinolaryngology. 2024;(3):24-28

Efficacy and safety of artificial intelligence-based large language models for decision making support in herniology: evaluation by experts and general surgeons. Pirogov Russian Journal of Surgery. 2024;(8):6-14

Application options of the artificial intelligence in colposcopy within screening programs. P.A. Herzen Journal of Oncology. 2024;(4):66-71

Experience of Data Analysis application for proceeding research data in solving the problem of target age group establishment. Forensic Medical Expertise. 2024;(4):37-41

The authors present the world experience of application of information technologies in intensive care units. The advantages of digitalization and patient data management systems are analyzed regarding clinical and economic efficiency. The authors describe the basic functionality of IntelliSpace Critical Care and Anesthesia (ICCA) patient data management system (Philips). The key issues of machine learning and artificial intelligence for analysis and forecasting in medicine are discussed.

Keywords:

information technology

anesthesiology

intensive care

ICCA

artificial intelligence

machine learning

Authors:

Ramaniuk T.I.

Philips LLC

Pozdnyakov D.Yu.

Philips LLC

Mushenok F.B.

Philips Innovation Laboratories Rus

Received:

26.03.2021

Accepted:

10.04.2021

List of references:

Fleischmann-Struzek C, Goldfarb DM, Schlattmann P, Schlapbach LJ, Reinhart K, Kissoon N. The global burden of pediatric and neonatal sepsis: a systematic review. The Lancet. Respiratory Medicine. 2018;6(3):223-230. https://doi.org/10.1016/S2213-2600(18)30063-8
Cheng L-F, Darnell G, Chivers C, Draugelis M, Li K, Engelhardt B. Sparse multi-output Gaussian processes for medical time series prediction. BMC Medical Informatics and Decision Making. 2020;20(1):152. https://doi.org/10.1186/s12911-020-1069-4
Rivers E, Nguyen B, Havstad S, Ressler J, Muzzin A, Knoblich B, Peterson E, Tomlanovich M; Early Goal-Directed Therapy Collaborative Group. Early goal-directed therapy in the treatment of severe sepsis and septic shock. The New England Journal of Medicine. 2001;345(19):1368-1377. https://doi.org/10.1056/NEJMoa010307
Mao Q, Jay M, Hoffman JL, Calvert J, Barton C, Shimabukuro D, Shieh L, Chettipally U, Fletcher G, Kerem Y, Zhou Y, Das R. Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU. BMJ Open. 2018;8(1):e017833. https://doi.org/10.1136/bmjopen-2017-017833
Lizotte DJ, Laber EB. Multi-objective Markov decision processes for data-driven decision support. Journal of Machine Learning Research: JMLR. 2016;17:211.
Meyer A, Zverinski D, Pfahringer B, Kempfert J, Kuehne T, Sündermann SH, Stamm C, Hofmann T, Falk V, Eickhoff C. Machine learning for ral-time prediction of complications in critical care: a retrospective study. The Lancet. Respiratory Medicine. 2018;6(12):905-914. https://doi.org/10.1016/S2213-2600(18)30300-X
Subbe CP, Duller B, Bellomo R. Effect of an automated notification system for deteriorating ward patients on clinical outcomes. Critical Care. 2017;21(1):52. https://doi.org/10.1186/s13054-017-1635-z
Johnson AE, Pollard T. J, Shen L, Lehman L-W. H, Feng M, Ghassemi M, Moody B, Szolovits P, Celi LA. Mark RG. Mimic-III, a freely accessible critical care database. Scientific Data. 2016;3:160035. https://doi.org/10.1038/sdata.2016.35
Vincent JL, Martin GS, Levy MM. QSOFA does not replace SIRS in the definition of sepsis. Critical Care. 2016;20(1):210. https://doi.org/10.1186/s13054-016-1389-z
De Backer D, Donadello K, Sakr Y, Ospina-Tascon G, Salgado D, Scolletta S, Vincent JL. Microcirculatory alterations in patients with severe sepsis: impact of time of assessment and relationship with outcome. Critical Care Medicine. 2013;41(3):791-799. https://doi.org/10.1097/CCM.0b013e3182742e8b
Cheng L-F, Prasad N, Engelhardt BE. An Optimal Policy for Patient Laboratory Tests in Intensive Care Units. Pacific Symposium on Biocomputing. 2019;24:320-331.
Ren O, Johnson A, Lehman E, Komorowski M, Aboab J, Fengyi Tang, Shahn Z, Sow D, Mark R, Li-Wei Lehman. Predicting and Understanding Unexpected Respiratory Decompensation in Critical Care Using Sparse and Heterogeneous Clinical Data. IEEE International Conference on Healthcare Informatics (ICHI). New York; 2018;144-151. https://doi.org/10.1109/ICHI.2018.00024
Shearer C. The CRISP-DM model: the new blueprint for data mining. Journal of Data Warehousing. 2000;5:13-22.
Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, Bellomo R, Bernard GR, Chiche JD, Coopersmith CM, Hotchkiss RS, Levy MM, Marshall JC, Martin GS, Opal SM, Rubenfeld GD, van der Poll T, Vincent JL, Angus DC. The third international consensus definitions for sepsis and septic shock (sepsis-3). JAMA. 2016;315(8):801-810. https://doi.org/10.1001/jama.2016.0287
Snow-cholera-map-1.jpg https://en.wikipedia.org/wiki/File:Snow-cholera-map-1.jpg
500 Person Gender-Height-Weight-Body Mass Index https://www.kaggle.com/yersever/500-person-gender-height-weight-bodymassindex

Close metadata