Literature DB >> 11269246

Artificial intelligence applications in the intensive care unit.

C W Hanson1, B E Marshall.   

Abstract

OBJECTIVE: To review the history and current applications of artificial intelligence in the intensive care unit. DATA SOURCES: The MEDLINE database, bibliographies of selected articles, and current texts on the subject. STUDY SELECTION: The studies that were selected for review used artificial intelligence tools for a variety of intensive care applications, including direct patient care and retrospective database analysis. DATA EXTRACTION: All literature relevant to the topic was reviewed. DATA SYNTHESIS: Although some of the earliest artificial intelligence (AI) applications were medically oriented, AI has not been widely accepted in medicine. Despite this, patient demographic, clinical, and billing data are increasingly available in an electronic format and therefore susceptible to analysis by intelligent software. Individual AI tools are specifically suited to different tasks, such as waveform analysis or device control.
CONCLUSIONS: The intensive care environment is particularly suited to the implementation of AI tools because of the wealth of available data and the inherent opportunities for increased efficiency in inpatient care. A variety of new AI tools have become available in recent years that can function as intelligent assistants to clinicians, constantly monitoring electronic data streams for important trends, or adjusting the settings of bedside devices. The integration of these tools into the intensive care unit can be expected to reduce costs and improve patient outcomes.

Entities:  

Mesh:

Year:  2001        PMID: 11269246     DOI: 10.1097/00003246-200102000-00038

Source DB:  PubMed          Journal:  Crit Care Med        ISSN: 0090-3493            Impact factor:   7.598


  24 in total

Review 1.  [Scoring systems in the intensive care unit].

Authors:  K Lewandowski; M Lewandowski
Journal:  Anaesthesist       Date:  2003-10       Impact factor: 1.041

2.  Parallel particle filters for online identification of mechanistic mathematical models of physiology from monitoring data: performance and real-time scalability in simulation scenarios.

Authors:  Sven Zenker
Journal:  J Clin Monit Comput       Date:  2010-07-31       Impact factor: 2.502

Review 3.  Developing and using expert systems and neural networks in medicine: a review on benefits and challenges.

Authors:  Abbas Sheikhtaheri; Farahnaz Sadoughi; Zahra Hashemi Dehaghi
Journal:  J Med Syst       Date:  2014-07-16       Impact factor: 4.460

4.  Future Challenges of Robotics and Artificial Intelligence in Nursing: What Can We Learn from Monsters in Popular Culture?

Authors:  Henrik Erikson; Martin Salzmann-Erikson
Journal:  Perm J       Date:  2016-07-15

5.  A comparison of graphical and textual presentations of time series data to support medical decision making in the neonatal intensive care unit.

Authors:  Anna S Law; Yvonne Freer; Jim Hunter; Robert H Logie; Neil McIntosh; John Quinn
Journal:  J Clin Monit Comput       Date:  2005-06       Impact factor: 2.502

6.  Robust model-based quantification of global ventricular torsion from spatially sparse three-dimensional time series data by orthogonal distance regression: evaluation in a canine animal model under different pacing regimes.

Authors:  Sven Zenker; Hyung Kook Kim; Gilles Clermont; Michael R Pinsky
Journal:  Pacing Clin Electrophysiol       Date:  2012-08-16       Impact factor: 1.976

Review 7.  Machine Learning and Artificial Intelligence in Neurocritical Care: a Specialty-Wide Disruptive Transformation or a Strategy for Success.

Authors:  Fawaz Al-Mufti; Michael Kim; Vincent Dodson; Tolga Sursal; Christian Bowers; Chad Cole; Corey Scurlock; Christian Becker; Chirag Gandhi; Stephan A Mayer
Journal:  Curr Neurol Neurosci Rep       Date:  2019-11-13       Impact factor: 5.081

8.  A tree-based decision model to support prediction of the severity of asthma exacerbations in children.

Authors:  Ken Farion; Wojtek Michalowski; Szymon Wilk; Dympna O'Sullivan; Stan Matwin
Journal:  J Med Syst       Date:  2009-03-11       Impact factor: 4.460

9.  Mismatched concepts in a neonatal intensive care unit (NICU): further issues for computer decision support?

Authors:  Yvonne Freer; Lindsey Ferguson; Gary Ewing; Jim Hunter; Robert Logie; Sue Rudkin; Neil McIntosh
Journal:  J Clin Monit Comput       Date:  2002-12       Impact factor: 2.502

10.  Prediction of mortality in an Indian intensive care unit. Comparison between APACHE II and artificial neural networks.

Authors:  Ashish Nimgaonkar; Dilip R Karnad; S Sudarshan; Lucila Ohno-Machado; Isaac Kohane
Journal:  Intensive Care Med       Date:  2004-01-15       Impact factor: 17.440

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