Machine learning programs predict risk of death based on results from routine hospital tests ualberta NaturePortfolio
. Raghunath et al reported a higher AUROC associated with their XGB model based on age and sex alone , while our model predicting 1-year mortality based on patient’s age and sex had an AUROC of 0.716. Our study used standard DL models and was based on ECGs from a single equipment manufacturer ; while the US study used custom-designed DL architecture and was based on ECGs from different equipment manufacturers.
DL models with convolutional neural networks are considered black boxes when it comes to identifying and interpreting patterns used by the model for prognostication. We have attempted techniques such as creating GradCAM heatmaps for that purpose, which suggest that PR intervals, QRS complexes and ST-T changes, especially the initial portion of the QRS complex contribute the most to mortality prediction.
In conclusion, our study demonstrates that ECG-based DL models can be used to identify patients who are at high risk for short- or longer-term mortality. These models perform equally well in males and females and can be augmented with the inclusion of data on routinely performed lab tests. Future studies are being planned to assess the utility of providing risk assessment based on ECG data in clinical practice.
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