Artificial intelligence applications in the intensive care unit

被引:124
作者
Hanson, CW [1 ]
Marshall, BE [1 ]
机构
[1] Univ Penn, Hlth Syst, Dept Anesthesia, Ctr Anesthesia Res, Philadelphia, PA 19104 USA
关键词
intensive care unit; artificial intelligence; expert systems; computer-assisted diagnosis; computer-assisted therapy; decision support techniques; neural networks; algorithms; fuzzy logic; data display; computer simulation; clinical decision; support systems; management decision support systems;
D O I
10.1097/00003246-200102000-00038
中图分类号
R4 [临床医学];
学科分类号
1002 ; 100602 ;
摘要
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, Al 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 Al 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 Al tools because of the wealth of available data and the inherent opportunities for increased efficiency in inpatient care. A variety of new Al 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.
引用
收藏
页码:427 / 435
页数:9
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