Literature DB >> 11958484

A review of evidence of health benefit from artificial neural networks in medical intervention.

P J G Lisboa1.   

Abstract

The purpose of this review is to assess the evidence of healthcare benefits involving the application of artificial neural networks to the clinical functions of diagnosis, prognosis and survival analysis, in the medical domains of oncology, critical care and cardiovascular medicine. The primary source of publications is PUBMED listings under Randomised Controlled Trials and Clinical Trials. The rĵle of neural networks is introduced within the context of advances in medical decision support arising from parallel developments in statistics and artificial intelligence. This is followed by a survey of published Randomised Controlled Trials and Clinical Trials, leading to recommendations for good practice in the design and evaluation of neural networks for use in medical intervention.

Mesh:

Year:  2002        PMID: 11958484     DOI: 10.1016/s0893-6080(01)00111-3

Source DB:  PubMed          Journal:  Neural Netw        ISSN: 0893-6080


  63 in total

Review 1.  Advances in electronic-nose technologies developed for biomedical applications.

Authors:  Alphus D Wilson; Manuela Baietto
Journal:  Sensors (Basel)       Date:  2011-01-19       Impact factor: 3.576

Review 2.  Modeling paradigms for medical diagnostic decision support: a survey and future directions.

Authors:  Kavishwar B Wagholikar; Vijayraghavan Sundararajan; Ashok W Deshpande
Journal:  J Med Syst       Date:  2011-10-01       Impact factor: 4.460

3.  Mammographic mass detection using wavelets as input to neural networks.

Authors:  Niyazi Kilic; Pelin Gorgel; Osman N Ucan; Ahmet Sertbas
Journal:  J Med Syst       Date:  2009-06-23       Impact factor: 4.460

4.  [International outcomes from attempts to implement a clinical decision support system in gastroenterology].

Authors:  Josceli Maria Tenório; Anderson Diniz Hummel; Vera Lucia Sdepanian; Ivan Torres Pisa; Heimar de Fátima Marin
Journal:  J Health Inform       Date:  2011 Jan-Mar

5.  Training neural network classifiers for medical decision making: the effects of imbalanced datasets on classification performance.

Authors:  Maciej A Mazurowski; Piotr A Habas; Jacek M Zurada; Joseph Y Lo; Jay A Baker; Georgia D Tourassi
Journal:  Neural Netw       Date:  2007-12-27

6.  Artificial neural networks in the recognition of the presence of thyroid disease in patients with atrophic body gastritis.

Authors:  Edith Lahner; Marco Intraligi; Massimo Buscema; Marco Centanni; Lucy Vannella; Enzo Grossi; Bruno Annibale
Journal:  World J Gastroenterol       Date:  2008-01-28       Impact factor: 5.742

7.  Artificial neural networks in prediction of bone density among post-menopausal women.

Authors:  M Sadatsafavi; A Moayyeri; A Soltani; B Larijani; M Nouraie; S Akhondzadeh
Journal:  J Endocrinol Invest       Date:  2005-05       Impact factor: 4.256

8.  Correlating tissue outcome with quantitative multiparametric MRI of acute cerebral ischemia in rats.

Authors:  Kimmo T Jokivarsi; Yrjö Hiltunen; Pasi I Tuunanen; Risto A Kauppinen; Olli H J Gröhn
Journal:  J Cereb Blood Flow Metab       Date:  2009-11-11       Impact factor: 6.200

9.  Polymorphisms in folate-metabolizing genes, chromosome damage, and risk of Down syndrome in Italian women: identification of key factors using artificial neural networks.

Authors:  Fabio Coppedè; Enzo Grossi; Francesca Migheli; Lucia Migliore
Journal:  BMC Med Genomics       Date:  2010-09-24       Impact factor: 3.063

10.  The use of artificial neural networks in prediction of congenital CMV outcome from sequence data.

Authors:  Ravit Arav-Boger; Yuval S Boger; Charles B Foster; Zvi Boger
Journal:  Bioinform Biol Insights       Date:  2008-05-29
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