Fig. 6: Feature importance and interactions for large-scale diagnostic features and PRSs in neural network models (NG2 + PRS; NN and NG2; NN).
LR logistic regression, NN neural network, NG1 non-genetic dataset 1 (known disease-specific risk factors), NG2 non-genetic dataset 2 (large-scale medical history embedding + known disease-specific risk factors), PRS polygenic risk score, SBP systolic blood pressure, BMI body mass index, Hx history, Diag diagnostic features (NG2 feature set excluding the NG1 features). a (Left) Scatterplot of the mean feature importance weights across ten trials of the 589 NG2 features for the NG2; NN (neural network) model and its counterpart NG2; LR (logistic regression) model. The features are highly correlated (Pearson r2 = 0.96). (Right) The same is shown for the multi-modal NG2 + PRS; NN (neural network) model and its counterpart logistic regression model. b Bar chart of the number of significant features for the NG2; LR model and its counterpart neural network model (NG2; NN). Feature significance was calculated using a one-sample two-tailed t-test comparing the mean and standard error of each feature importance weight across ten trials to zero, followed by the Bonferroni correction (alpha = 0.01) based on the 589 total features. Features with weights significantly greater than or less than zero were “significant”. c A boxplot is shown with the distribution of feature importance weight for each of the positive significant features across the trials for the NG2 + PRS; NN model, color-coded by the feature data modality. d Boxplot comparing each type of pairwise interaction between features in the NG2 + PRS; NN model. e Bar chart showing mean interaction weight for the top pairwise interactions in the NG2 + PRS; NN model, color-coded by type of pairwise interaction, with 95% confidence intervals and per-trial weights for each feature. f The top 0.1% of the 175,528 possible pairwise groupings of features in the NG2 + PRS; NN model with the highest feature interaction weights are shown in a stacked bar chart, split based on the proportion of each type of pairwise grouping. The top 0.1% interacting pairs of features are enriched for Diag–NG1, Diag–Diag, and NG1–NG1 interactions. The bottom 0.1% are also shown and are most heavily enriched for Diag–NG1 and Diag–Diag interactions.
