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

Abstract

ObjectiveThe 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.MethodaLYNX 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.ResultsThe 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)].ConclusionThe 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.

Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone

“…To make our predictions more realistic, we avoided using the same balance ratio of the whole complete dataset (32.11% positives and 67.89% negatives). This way, we had different balance ratios for each of the 100 executions with, on average, 32.06% positives and 66.94% negatives on average in the training sets, and with, on average, 32 [103,104] we obtained the global list ( Fig. 2 for N = 100), together with the Borda count score of each feature, corresponding to the average position across all 7N lists, and thus the lower the score, the more important the feature.…”

“…Computational intelligence, especially, shows its predictive power when applied to medical records [25,26], or coupled with imaging [27][28][29]. Further, deep learning and meta-analysis studies applied to this field have also recently appeared in the literature [30][31][32][33], improving on human specialists' performance [34], albeit showing lower accuracy (0.75 versus 0.59).…”

Optimization of multiplex quantitative polymerase chain reaction based on response surface methodology and an artificial neural network-genetic algorithm approach

“…Subsequently, prediction models for the tested factors and Ct values are constructed via RSM and BPNN-GA respectively. The prediction performance of these two models is then evaluated using the coefficient of determination (R 2 ), the mean absolute error (MAE), and the mean square error (MSE)[ 20 – 22 ]. The model resulting in better prediction performance is further tested for condition optimization.…”

Artificial intelligence—Developments in medicine in the last two years

“…connections of brain regions in children with autism 22 ) Classification, clustering, segmentation Monitoring and control Telemedicine 23 Text analysis and language processing Speech intentions from online conversation 24 Medical text semantics 25 Statistics from death certificates 26 Automatic classification of radiological reports 27 Classification of multilingual biomedical documents 28 Relation classification in medical records 23 Natural language processing tasks (e.g. summarization, text classification, relation extraction) Devices and gadgets Internet of Health Things (IoHT) 29 Smart home and early anomaly detection in elderly 30 Portable devices and mobile applications Decision support and expert systems Retinopathy by arteriovenous ratio 31 Coronary bypass surgery vs coronary stenting 32 Diagnostics Diagnostic labeling 33 Diagnosis by pattern recognition 34 Fatigue by eye tracking 35 > Correlations between diseases Early anomaly detection in behavior 30 Early indicators of Parkinson's disease progression 36 Treatment Therapy (e.g. automatic anesthesia 37 ) Surgery (e.g.…”

“…There are a lot of local registries in any regional hospital that are available and can be used for machine learning decision support. 32 …”