Document Detail


Artificial intelligence techniques: An efficient new approach to challenge the assessment of complex clinical fields such as airway clearance techniques in patients with cystic fibrosis?
MedLine Citation:
PMID:  23420272     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
OBJECTIVE: To construct an artificial intelligence application to assist untrained physiotherapists in determining the appropriate physiotherapy exercises to improve the quality of life of patients with cystic fibrosis.
SUBJECTS: A total of 42 children (21 boys and 21 girls), age range 6-18 years, participated in a clinical survey between 2001 and 2005.
METHODS: Data collected during the clinical survey were entered into a neural network in order to correlate the health state indicators of the patients and the type of physiotherapy exercise to be followed. Cross-validation of the network was carried out by comparing the health state indicators achieved after following a certain physiotherapy exercise and the health state indicators predicted by the network.
RESULTS: The lifestyle and health state indicators of the survey participants improved. The network predicted the health state indicators of the participants with an accuracy of 93%. The results of the cross-validation test were within the error margins of the real-life indicators.
CONCLUSION: Using data on the clinical state of individuals with cystic fibrosis, it is possible to determine the most effective type of physiotherapy exercise for improving overall health state indicators.
Authors:
Titus Slavici; Bogdan Almajan
Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't; Validation Studies    
Journal Detail:
Title:  Journal of rehabilitation medicine     Volume:  45     ISSN:  1651-2081     ISO Abbreviation:  J Rehabil Med     Publication Date:  2013 Apr 
Date Detail:
Created Date:  2013-04-02     Completed Date:  2013-07-09     Revised Date:  2014-05-30    
Medline Journal Info:
Nlm Unique ID:  101088169     Medline TA:  J Rehabil Med     Country:  Sweden    
Other Details:
Languages:  eng     Pagination:  397-402     Citation Subset:  IM    
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Descriptor/Qualifier:
Adolescent
Artificial Intelligence*
Child
Cystic Fibrosis / therapy*
Female
Health Status Indicators
Humans
Male
Neural Networks (Computer)*
Respiratory Therapy / methods*

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


Previous Document:  Immunohistochemistry and fluorescence in situ hybridization assessment of HER2 in clinical trials of...
Next Document:  Factor structure and longitudinal invariance of the Center for Epidemiological Studies Depression Sc...