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Critical Care
Published in: Critical Care 1/2019

Open Access 01-12-2019 | Septicemia | Editorial

Artificial intelligence in the intensive care unit

Authors: Christopher A. Lovejoy, Varun Buch, Mahiben Maruthappu

Published in: Critical Care | Issue 1/2019

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Excerpt

The use of artificial intelligence (AI) in healthcare is receiving increasing interest, driven by a surge in scientific research and funding. AI has shown ophthalmologist-level performance at detecting retinal pathology [1] and can provide individualised treatment decisions for sepsis that could improve patient outcomes [2]. There are many potential applications in the intensive care unit (ICU), particularly given the large amounts of data collected routinely. However, there are some important considerations for ensuring successful implementation. …
Literature
1.
De Fauw J, Ledsam JR, Romera-Paredes B, Nikolov S, Tomasev N, Blackwell S, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med. 2018;24:1342–50.CrossRef
2.
Komorowski M, Celi LA, Badawi O, Gordon AC, Faisal AA. The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care. Nat Med. 2018;24:1716.CrossRef
3.
Strand K, Flaatten H. Severity scoring in the ICU: a review. Acta Anaesthesiol Scand. 2008;52:467–78.CrossRef
4.
Dybowski R, Gant V, Weller P, Chang R. Prediction of outcome in critically ill patients using artificial neural network synthesised by genetic algorithm. Lancet. 1996;347:1146–50.CrossRef
5.
Pirracchio R, Petersen ML, Carone M, Rigon MR, Chevret S, van der Laan MJ. Mortality prediction in intensive care units with the Super ICU Learner Algorithm (SICULA): a population-based study. Lancet Respir Med. 2015;3:42–52.CrossRef
6.
Aczon M, Ledbetter D, Ho L, Gunny A, Flynn A, Williams J, et al. Dynamic Mortality Risk Predictions in Pediatric Critical Care Using Recurrent Neural Networks. ArXiv170106675 Cs Math Q-Bio Stat. 2017 Jan 23; Available from: http://​arxiv.​org/​abs/​1701.​06675. Accessed 4 Jan 2019.
7.
Desautels T, Calvert J, Hoffman J, Jay M, Kerem Y, Shieh L, et al. Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach. JMIR Med Inform. 2016;4:e28.CrossRef
8.
Nemati S, Holder A, Razmi F, Stanley MD, Clifford GD, Buchman TG. An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU. Crit Care Med. 2018;46:547–53.CrossRef
9.
Kamaleswaran R, Akbilgic O, Hallman MA, West AN, Davis RL, Shah SH. Applying Artificial Intelligence to Identify Physiomarkers Predicting Severe Sepsis in the PICU. Pediatr Crit Care Med. 2018;19:e495–503.CrossRef
10.
Prasad N, Cheng L-F, Chivers C, Draugelis M, Engelhardt BE. A Reinforcement Learning Approach to Weaning of Mechanical Ventilation in Intensive Care Units. ArXiv170406300 Cs. 2017 Apr 20; Available from: http://​arxiv.​org/​abs/​1704.​06300. Accessed 4 Jan 2019.
11.
Parreco J, Hidalgo A, Parks JJ, Kozol R, Rattan R. Using artificial intelligence to predict prolonged mechanical ventilation and tracheostomy placement. J Surg Res. 2018;228:179–87.CrossRef
12.
Davoudi A, Malhotra KR, Shickel B, Siegel S, Williams S, Ruppert M, et al. The Intelligent ICU Pilot Study: Using Artificial Intelligence Technology for Autonomous Patient Monitoring. ArXiv180410201 Cs Eess. 2018 Apr 25; Available from: http://​arxiv.​org/​abs/​1804.​10201. Accessed 4 Jan 2019.
13.
Pickham D, Berte N, Pihulic M, Valdez A, Mayer B, Desai M. Effect of a wearable patient sensor on care delivery for preventing pressure injuries in acutely ill adults: A pragmatic randomized clinical trial (LS-HAPI study). Int J Nurs Stud. 2018;80:12–9.CrossRef
14.
Afsar FA, Arif M, Yang J. Detection of ST segment deviation episodes in ECG using KLT with an ensemble neural classifier. Physiol Meas. 2008;29:747–60.CrossRef
15.
Rajpurkar P, Hannun AY, Haghpanahi M, Bourn C, Ng AY. Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks. ArXiv170701836 Cs. 2017 Jul 6; Available from: http://​arxiv.​org/​abs/​1707.​01836. Accessed 4 Jan 2019.
Metadata
Title
Artificial intelligence in the intensive care unit
Authors
Christopher A. Lovejoy
Varun Buch
Mahiben Maruthappu
Publication date
01-12-2019
Publisher
BioMed Central
Published in
Critical Care / Issue 1/2019
Electronic ISSN: 1364-8535
DOI
https://doi.org/10.1186/s13054-018-2301-9

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