Artificial intelligence to support clinical decision-making processes

Completed as part of a larger dissertational study, the purpose of this portion of this descriptive correlational study was to examine the relationships among registered nurses’ clinical experiences and clinical decision-making processes in the critical care environment. The results indicated that there is no strong correlation between clinical experience in general and clinical experience in critical care and clinical decision-making. There were no differences found in any of the Benner stages of clinical experience in relation to the overall clinical decision-making process.

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The Clinical Decision-Making Processes of Student Nurses

Journal of Nursing Education ◽

10.3928/0148-4834-19931101-04 ◽

1993 ◽

Vol 32 (9) ◽

pp. 389-398 ◽

Cited By ~ 3

Author(s):

Sharon Tschikota

Keyword(s):

Decision Making ◽

Clinical Decision Making ◽

Student Nurses ◽

Clinical Decision ◽

Decision Making Processes

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Can artificial Intelligence Support Clinical Decision Making in the Management of Hepatocellular Carcinoma Patients? (Preprint)

JMIR Cancer ◽

10.2196/21074 ◽

2020 ◽

Author(s):

Zebin Chen ◽

Kaiyu Sun ◽

Ruiming Liang ◽

Zhenwei Peng ◽

Jingxian Shen ◽

...

Keyword(s):

Artificial Intelligence ◽

Hepatocellular Carcinoma ◽

Decision Making ◽

Clinical Decision Making ◽

Clinical Decision ◽

Intelligence Support

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The Use of Artificial Intelligence Systems for Support of Medical Decision-Making

Clinical Technologies ◽

10.4018/978-1-60960-561-2.ch402 ◽

2011 ◽

pp. 1017-1029

Author(s):

William Claster ◽

Nader Ghotbi ◽

Subana Shanmuganathan

Keyword(s):

Artificial Intelligence ◽

Decision Making ◽

Statistical Analysis ◽

Medical Records ◽

Clinical Decision Making ◽

Ct Scanning ◽

Clinical Decision ◽

Radiology Department ◽

Self Organizing Map ◽

One Year

There is a treasure trove of hidden information in the textual and narrative data of medical records that can be deciphered by text-mining techniques. The information provided by these methods can provide a basis for medical artificial intelligence and help support or improve clinical decision making by medical doctors. In this paper we extend previous work in an effort to extract meaningful information from free text medical records. We discuss a methodology for the analysis of medical records using some statistical analysis and the Kohonen Self-Organizing Map (SOM). The medical data derive from about 700 pediatric patients’ radiology department records where CT (Computed Tomography) scanning was used as part of a diagnostic exploration. The patients underwent CT scanning (single and multiple) throughout a one-year period in 2004 at the Nagasaki University Medical Hospital. Our approach led to a model based on SOM clusters and statistical analysis which may suggest a strategy for limiting CT scan requests. This is important because radiation at levels ordinarily used for CT scanning may pose significant health risks especially to children.

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The Role of Artificial Intelligence in Clinical Decision Support Systems and a Classification Framework

Robotic Systems ◽

10.4018/978-1-7998-1754-3.ch008 ◽

2020 ◽

pp. 167-186

Author(s):

Steven Walczak

Keyword(s):

Artificial Intelligence ◽

Decision Making ◽

Decision Support ◽

Decision Support Systems ◽

Clinical Decision Support ◽

Clinical Decision Making ◽

Support Systems ◽

Clinical Decision Support Systems ◽

Clinical Decision ◽

Classification Framework

Clinical decision support systems are meant to improve the quality of decision-making in healthcare. Artificial intelligence is the science of creating intelligent systems that solve complex problems at the level of or better than human experts. Combining artificial intelligence methods into clinical decision support will enable the utilization of large quantities of data to produce relevant decision-making information to practitioners. This article examines various artificial intelligence methodologies and shows how they may be incorporated into clinical decision-making systems. A framework for describing artificial intelligence applications in clinical decision support systems is presented.

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