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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2016-09-01
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| Series: | Chronic Diseases and Translational Medicine |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2095882X16300238 |
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| 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 |
| collection | DOAJ |
| 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 |
| first_indexed | 2024-12-12T13:30:37Z |
| format | Article |
| id | doaj.art-3a0e63fd3c17456ebbcd181af3995e32 |
| institution | Directory Open Access Journal |
| issn | 2095-882X |
| language | English |
| last_indexed | 2024-12-12T13:30:37Z |
| publishDate | 2016-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | Chronic Diseases and Translational Medicine |
| spelling | doaj.art-3a0e63fd3c17456ebbcd181af3995e322022-12-22T00:23:04ZengWileyChronic Diseases and Translational Medicine2095-882X2016-09-0123166172Artificial intelligence: Neural network model as the multidisciplinary team member in clinical decision support to avoid medical mistakesIgor Vyacheslavovich Buzaev0Vladimir Vyacheslavovich Plechev1Irina Evgenievna Nikolaeva2Rezida Maratovna Galimova3Interventional Cardiology 1 Department, GBUZ Republic Heart Centre, Ufa, Russia; Bashkir State Medical University, Ufa, Russia; Corresponding author. kv. 47, d. 54, Kommunisticheskaya str., Ufa, 450077, Russia.Bashkir State Medical University, Ufa, RussiaGBUZ Republic Heart Centre, Ufa, RussiaBashkir State Medical University, Ufa, RussiaObjective: 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 makinghttp://www.sciencedirect.com/science/article/pii/S2095882X16300238 |
| spellingShingle | 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 Chronic Diseases and Translational Medicine |
| title | 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_short | 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 |
| url | http://www.sciencedirect.com/science/article/pii/S2095882X16300238 |
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