Literature DB >> 18690868

Applications of artificial neural networks in medical science.

Jigneshkumar L Patel1, Ramesh K Goyal.   

Abstract

Computer technology has been advanced tremendously and the interest has been increased for the potential use of 'Artificial Intelligence (AI)' in medicine and biological research. One of the most interesting and extensively studied branches of AI is the 'Artificial Neural Networks (ANNs)'. Basically, ANNs are the mathematical algorithms, generated by computers. ANNs learn from standard data and capture the knowledge contained in the data. Trained ANNs approach the functionality of small biological neural cluster in a very fundamental manner. They are the digitized model of biological brain and can detect complex nonlinear relationships between dependent as well as independent variables in a data where human brain may fail to detect. Nowadays, ANNs are widely used for medical applications in various disciplines of medicine especially in cardiology. ANNs have been extensively applied in diagnosis, electronic signal analysis, medical image analysis and radiology. ANNs have been used by many authors for modeling in medicine and clinical research. Applications of ANNs are increasing in pharmacoepidemiology and medical data mining. In this paper, authors have summarized various applications of ANNs in medical science.

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Mesh:

Year:  2007        PMID: 18690868     DOI: 10.2174/157488407781668811

Source DB:  PubMed          Journal:  Curr Clin Pharmacol        ISSN: 1574-8847


  43 in total

1.  The effect of artificial neural network model combined with six tumor markers in auxiliary diagnosis of lung cancer.

Authors:  Feifei Feng; Yiming Wu; Yongjun Wu; Guangjin Nie; Ran Ni
Journal:  J Med Syst       Date:  2011-09-01       Impact factor: 4.460

2.  Artificial neural networks for small dataset analysis.

Authors:  Antonello Pasini
Journal:  J Thorac Dis       Date:  2015-05       Impact factor: 2.895

Review 3.  Arterial Stiffness and Coronary Ischemia: New Aspects and Paradigms.

Authors:  Alexandre Vallée; Alexandre Cinaud; Athanase Protogerou; Yi Zhang; Jirar Topouchian; Michel E Safar; Jacques Blacher
Journal:  Curr Hypertens Rep       Date:  2020-01-10       Impact factor: 5.369

4.  Analysis of spontaneous pneumothorax in the city of Cuneo: environmental correlations with meteorological and air pollutant variables.

Authors:  Luca Bertolaccini; Andrea Viti; Lucia Boschetto; Antonello Pasini; Alessandro Attanasio; Alberto Terzi; Claudio Cassardo
Journal:  Surg Today       Date:  2014-08-19       Impact factor: 2.549

Review 5.  A Cardio-Oncology Data Commons: Lessons from Pediatric Oncology.

Authors:  Anant Mandawat; Logan Eberly; William Border
Journal:  Curr Cardiol Rep       Date:  2019-09-13       Impact factor: 2.931

6.  Artificial neural network for the prediction model of glomerular filtration rate to estimate the normal or abnormal stages of kidney using gamma camera.

Authors:  Alamgir Hossain; Shariful Islam Chowdhury; Shupti Sarker; Mostofa Shamim Ahsan
Journal:  Ann Nucl Med       Date:  2021-09-07       Impact factor: 2.668

7.  Differential diagnostics of Thalassemia Minor by artificial neural networks model.

Authors:  Guy Barnhart-Magen; Victor Gotlib; Rafael Marilus; Yulia Einav
Journal:  J Clin Lab Anal       Date:  2013-11       Impact factor: 2.352

8.  Pitfalls of supervised feature selection.

Authors:  Pawel Smialowski; Dmitrij Frishman; Stefan Kramer
Journal:  Bioinformatics       Date:  2009-10-29       Impact factor: 6.937

9.  Use of capnography for prediction of obstruction severity in non-intubated COPD and asthma patients.

Authors:  Barak Pertzov; Michal Ronen; Dror Rosengarten; Dorit Shitenberg; Moshe Heching; Yael Shostak; Mordechai R Kramer
Journal:  Respir Res       Date:  2021-05-21

10.  Comparison of hospital charge prediction models for gastric cancer patients: neural network vs. decision tree models.

Authors:  Jing Wang; Man Li; Yun-tao Hu; Yu Zhu
Journal:  BMC Health Serv Res       Date:  2009-09-14       Impact factor: 2.655

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