Literature DB >> 34305278

Artificial intelligence in critical care: Its about time!

Rashmi Datta1, Shalendra Singh2.   

Abstract

Currently, most critical care information is not expressed automatically at a granular level, rather is continually assessed by overindulged Intensive Care Unit (ICU) staff. Furthermore, due to different confounding morbidities and the uniqueness of the ICU setting, it is difficult to protocolize treatment regimens in the ICU. In highly complex ICU setting where man and resource management becomes extremely challenging, definite advancements are required to implement Artificial Intelligence (AI) for prognosticating the course of the disease to aid in informed decision-making. AI is the intelligence of a computer or computer-supervised robot to execute a piece of work commonly associated with intelligent beings, wherein the machines go beyond the realms of normal information processing by adding the characteristics of learning, sound reasoning, and weighting of the inputs. AI recognizes circuitous, relational time-series blueprint within datasets and this reasoning of analysis transcends conventional threshold-based analysis adapted in ICU protocols. AI works on the principle of a more complex form of Machine Learning by Artificial Neural Networks (ANN). These information-processing paradigms use multidimensional arrays called tensors which aid in 'learning' and 'weighting' all the information made available to it, thereby converting normal machine learning into Deep Learning. Here, the use of AI for data mining in complex ICU settings for protocol formulation and temporal representation and reasoning is discussed.
© 2021 Director General, Armed Forces Medical Services. Published by Elsevier, a division of RELX India Pvt. Ltd.

Entities:  

Keywords:  Artificial intelligence; Critical care; Intensive care unit

Year:  2021        PMID: 34305278      PMCID: PMC8282528          DOI: 10.1016/j.mjafi.2020.10.005

Source DB:  PubMed          Journal:  Med J Armed Forces India        ISSN: 0377-1237


  25 in total

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Review 3.  Deep learning in spiking neural networks.

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4.  Big data and machine learning in critical care: Opportunities for collaborative research.

Authors:  Antonio Núñez Reiz; Fernando Martínez Sagasti; Manuel Álvarez González; Antonio Blesa Malpica; Juan Carlos Martín Benítez; Mercedes Nieto Cabrera; Ángela Del Pino Ramírez; José Miguel Gil Perdomo; Jesús Prada Alonso; Leo Anthony Celi; Miguel Ángel Armengol de la Hoz; Rodrigo Deliberato; Kenneth Paik; Tom Pollard; Jesse Raffa; Felipe Torres; Julio Mayol; Joan Chafer; Arturo González Ferrer; Ángel Rey; Henar González Luengo; Giuseppe Fico; Ivana Lombroni; Liss Hernandez; Laura López; Beatriz Merino; María Fernanda Cabrera; María Teresa Arredondo; María Bodí; Josep Gómez; Alejandro Rodríguez; Miguel Sánchez García
Journal:  Med Intensiva (Engl Ed)       Date:  2018-08-02

5.  Dermatologist-level classification of skin cancer with deep neural networks.

Authors:  Andre Esteva; Brett Kuprel; Roberto A Novoa; Justin Ko; Susan M Swetter; Helen M Blau; Sebastian Thrun
Journal:  Nature       Date:  2017-01-25       Impact factor: 49.962

6.  Mortality prediction in intensive care units (ICUs) using a deep rule-based fuzzy classifier.

Authors:  Raheleh Davoodi; Mohammad Hassan Moradi
Journal:  J Biomed Inform       Date:  2018-02-19       Impact factor: 6.317

Review 7.  A review of supervised machine learning applied to ageing research.

Authors:  Fabio Fabris; João Pedro de Magalhães; Alex A Freitas
Journal:  Biogerontology       Date:  2017-03-06       Impact factor: 4.277

Review 8.  Artificial intelligence in healthcare: past, present and future.

Authors:  Fei Jiang; Yong Jiang; Hui Zhi; Yi Dong; Hao Li; Sufeng Ma; Yilong Wang; Qiang Dong; Haipeng Shen; Yongjun Wang
Journal:  Stroke Vasc Neurol       Date:  2017-06-21

9.  Shaping embodied neural networks for adaptive goal-directed behavior.

Authors:  Zenas C Chao; Douglas J Bakkum; Steve M Potter
Journal:  PLoS Comput Biol       Date:  2008-03-28       Impact factor: 4.475

10.  Using recurrent neural network models for early detection of heart failure onset.

Authors:  Edward Choi; Andy Schuetz; Walter F Stewart; Jimeng Sun
Journal:  J Am Med Inform Assoc       Date:  2017-03-01       Impact factor: 4.497

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