Artificial intelligence: Neural network model as the multidisciplinary team member in clinical decision support to avoid medical mistakes

Objective: The continuous uninterrupted feedback system is the essential part of any well-organized system. We propose aLYNX concept that is a possibility to use an artificial intelligence algorithm or a neural network model in decision-making system so as to avoid possible mistakes and to remind th...

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Autores principales:	Igor Vyacheslavovich Buzaev, Vladimir Vyacheslavovich Plechev, Irina Evgenievna Nikolaeva, Rezida Maratovna Galimova
Formato:	article
Lenguaje:	EN
Publicado:	KeAi Communications Co., Ltd. 2016
Materias:	Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/3a0e63fd3c17456ebbcd181af3995e32
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spelling	oai:doaj.org-article:3a0e63fd3c17456ebbcd181af3995e322021-12-02T13:25:08ZArtificial intelligence: Neural network model as the multidisciplinary team member in clinical decision support to avoid medical mistakes2095-882X10.1016/j.cdtm.2016.09.007https://doaj.org/article/3a0e63fd3c17456ebbcd181af3995e322016-09-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2095882X16300238https://doaj.org/toc/2095-882XObjective: The continuous uninterrupted feedback system is the essential part of any well-organized system. We propose aLYNX concept that is a possibility to use an artificial intelligence algorithm or a neural network model in decision-making system so as to avoid possible mistakes and to remind the doctors to review tactics once more in selected cases. Method: aLYNX system includes: registry with significant factors, decisions and results; machine learning process based on this registry data; the use of the machine learning results as the adviser. We show a possibility to build a computer adviser with a neural network model for making a choice between coronary aortic bypass surgery (CABG) and percutaneous coronary intervention (PCI) in order to achieve a higher 5-year survival rate in patients with angina based on the experience of 5107 patients. Results: The neural network was trained by 4679 patients who achieved 5-year survival. Among them, 2390 patients underwent PCI and 2289 CABG. After training, the correlation coefficient (r) of the network was 0.74 for training, 0.67 for validation, 0.71 for test and 0.73 for total. Simulation of the neural network function has been performed after training in the two groups of patients with known 5-year outcome. The disagreement rate was significantly higher in the dead patient group than that in the survivor group between neural network model and heart team [16.8% (787/4679) vs. 20.3% (87/428), P = 0.065)]. Conclusion: The study shows the possibility to build a computer adviser with a neural network model for making a choice between CABG and PCI in order to achieve a higher 5-year survival rate in patients with angina. Keywords: Coronary artery bypass grafting, Percutaneous coronary intervention, Artificial intelligence, Decision makingIgor Vyacheslavovich BuzaevVladimir Vyacheslavovich PlechevIrina Evgenievna NikolaevaRezida Maratovna GalimovaKeAi Communications Co., Ltd.articleMedicine (General)R5-920ENChronic Diseases and Translational Medicine, Vol 2, Iss 3, Pp 166-172 (2016)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine (General) R5-920
spellingShingle	Medicine (General) R5-920 Igor Vyacheslavovich Buzaev Vladimir Vyacheslavovich Plechev Irina Evgenievna Nikolaeva Rezida Maratovna Galimova Artificial intelligence: Neural network model as the multidisciplinary team member in clinical decision support to avoid medical mistakes
description	Objective: The continuous uninterrupted feedback system is the essential part of any well-organized system. We propose aLYNX concept that is a possibility to use an artificial intelligence algorithm or a neural network model in decision-making system so as to avoid possible mistakes and to remind the doctors to review tactics once more in selected cases. Method: aLYNX system includes: registry with significant factors, decisions and results; machine learning process based on this registry data; the use of the machine learning results as the adviser. We show a possibility to build a computer adviser with a neural network model for making a choice between coronary aortic bypass surgery (CABG) and percutaneous coronary intervention (PCI) in order to achieve a higher 5-year survival rate in patients with angina based on the experience of 5107 patients. Results: The neural network was trained by 4679 patients who achieved 5-year survival. Among them, 2390 patients underwent PCI and 2289 CABG. After training, the correlation coefficient (r) of the network was 0.74 for training, 0.67 for validation, 0.71 for test and 0.73 for total. Simulation of the neural network function has been performed after training in the two groups of patients with known 5-year outcome. The disagreement rate was significantly higher in the dead patient group than that in the survivor group between neural network model and heart team [16.8% (787/4679) vs. 20.3% (87/428), P = 0.065)]. Conclusion: The study shows the possibility to build a computer adviser with a neural network model for making a choice between CABG and PCI in order to achieve a higher 5-year survival rate in patients with angina. Keywords: Coronary artery bypass grafting, Percutaneous coronary intervention, Artificial intelligence, Decision making
format	article
author	Igor Vyacheslavovich Buzaev Vladimir Vyacheslavovich Plechev Irina Evgenievna Nikolaeva Rezida Maratovna Galimova
author_facet	Igor Vyacheslavovich Buzaev Vladimir Vyacheslavovich Plechev Irina Evgenievna Nikolaeva Rezida Maratovna Galimova
author_sort	Igor Vyacheslavovich Buzaev
title	Artificial intelligence: Neural network model as the multidisciplinary team member in clinical decision support to avoid medical mistakes
title_short	Artificial intelligence: Neural network model as the multidisciplinary team member in clinical decision support to avoid medical mistakes
title_full	Artificial intelligence: Neural network model as the multidisciplinary team member in clinical decision support to avoid medical mistakes
title_fullStr	Artificial intelligence: Neural network model as the multidisciplinary team member in clinical decision support to avoid medical mistakes
title_full_unstemmed	Artificial intelligence: Neural network model as the multidisciplinary team member in clinical decision support to avoid medical mistakes
title_sort	artificial intelligence: neural network model as the multidisciplinary team member in clinical decision support to avoid medical mistakes
publisher	KeAi Communications Co., Ltd.
publishDate	2016
url	https://doaj.org/article/3a0e63fd3c17456ebbcd181af3995e32
work_keys_str_mv	AT igorvyacheslavovichbuzaev artificialintelligenceneuralnetworkmodelasthemultidisciplinaryteammemberinclinicaldecisionsupporttoavoidmedicalmistakes AT vladimirvyacheslavovichplechev artificialintelligenceneuralnetworkmodelasthemultidisciplinaryteammemberinclinicaldecisionsupporttoavoidmedicalmistakes AT irinaevgenievnanikolaeva artificialintelligenceneuralnetworkmodelasthemultidisciplinaryteammemberinclinicaldecisionsupporttoavoidmedicalmistakes AT rezidamaratovnagalimova artificialintelligenceneuralnetworkmodelasthemultidisciplinaryteammemberinclinicaldecisionsupporttoavoidmedicalmistakes
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Artificial intelligence: Neural network model as the multidisciplinary team member in clinical decision support to avoid medical mistakes

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