scholarly journals Statistical Analysis of Gait Data to Assist Clinical Decision Making

2010 ◽  
pp. 61-68 ◽  
Author(s):  
Nigar Şen Köktaş ◽  
Robert P. W. Duin
Keyword(s):  
Decision Making ◽  
Statistical Analysis ◽  
Clinical Decision Making ◽  
Clinical Decision

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

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.

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

2010 ◽  
pp. 18-30 ◽  
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.

Clinical Decision Making and Oral Motor Treatments

ASHA Leader ◽  
2005 ◽  
Vol 10 (8) ◽  
pp. 8-35 ◽  
Author(s):  
Heather M. Clark
Keyword(s):  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Oral Motor

Debates in Dysphagia Management: How Do You Use Evidence-Based Practice in Your Dysphagia Patient Care?

2011 ◽  
Vol 20 (4) ◽  
pp. 121-123
Author(s):  
Jeri A. Logemann
Keyword(s):  
Decision Making ◽  
Patient Care ◽  
Clinical Outcomes ◽  
Clinical Judgment ◽  
Clinical Decision Making ◽  
Evidence Based Practice ◽  
Clinical Decision ◽  
Evidence Based ◽  
Clinical Method ◽  
Do So

Evidence-based practice requires astute clinicians to blend our best clinical judgment with the best available external evidence and the patient's own values and expectations. Sometimes, we value one more than another during clinical decision-making, though it is never wise to do so, and sometimes other factors that we are unaware of produce unanticipated clinical outcomes. Sometimes, we feel very strongly about one clinical method or another, and hopefully that belief is founded in evidence. Some beliefs, however, are not founded in evidence. The sound use of evidence is the best way to navigate the debates within our field of practice.

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