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The feedback is the essential part of any system. aLYNX concept is an idea to use some fuzzy logic algorithm, for example, a neural network model in decision-making system to avoid possible mistakes in a choice between PCI and CABG.
aLYNX system contains:
first - a registry with parameters, decisions, and late results;
second - machine learning process based on successful cases registry data;
third - the use of the machine learning results as the adviser.
Objective: To show a possibility to build a mathematic model as an adviser for making a decision between CABG and PCI based on the experience of 5107 patients.
The neural network was trained by 4,679 patients who achieved 5-year survival. Among them, 2,390 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 a total. Simulation of the neural network function has been performed after training in the two groups of patients with a known 5-year outcome. The disagreement rate was significantly higher in the dead patient group than that in the survivor group between a neural network model and heart team [16.8% (787/4679) vs. 20.3% (87/428), P=0.065)].
aLYNX concept shows the possibility to use a neural network model in decision-making to remind the doctors to review tactics once more in selected cases. Such system should include registry with significant factors, decisions, and results; machine learning process based on the registry data; using the machine learning results as the adviser.